Integrated Approach to Hydrogeochemical Assessment of Groundwater Quality in Major Industrial Zone of Punjab, Pakistan

Groundwater contamination with arsenic (As) is a significant concern in Pakistan's Punjab Province. This study analyzed 69 groundwater samples from Faisalabad, Gujranwala, Lahore, and Multan to understand hydrogeochemistry, health impacts, contamination sources, and drinking suitability. Results revealed varying as concentrations across districts, with distinctive cation and anion orders. Faisalabad exhibited Na+ > Mg2+ > Ca2+ > K+ > Fe2+ for cations and SO42- > Cl- > HCO3- > NO3- > F- for anions. Gujranwala showed Na+ > Ca2+ > Mg2+ > K+ for cations and HCO3-  > SO42- > Cl- > NO3-  > F- for anions. In Lahore, demonstrated: Na+ > Ca2+ > Mg2+ > Fe > K+ for cations and HCO3-  > SO42- > Cl- > NO3-  > F- for anions. Multan indicated K+ > Ca2+ > Mg2+ > Na+ > Fe for cations and HCO3-  > SO42- > Cl- > F- > NO3- ) for anions. Hydrochemical facies were identified as CaHCO3 and CaMgCl types. Principal Component Analysis (PCA), highlighted the influence of natural processes and human activities on groundwater pollution. Water Quality Index (WQI) result reveal that most samples met water quality standards. The carcinogenic risk values for children exceeded permissible limits in all districts, emphasizing a significant cancer risk. The study highlights the need for rigorous monitoring to mitigate (As) contamination and protect public health from associated hazards.

Occurrence of toxic elements in river areas along drains and groundwater resources: source of contamination and associated health risk

The objective of the current research was to examine the water quality of the River Ravi and the River Sutlej, with a specific focus on potentially toxic elements (PTEs). Additionally, we sought to monitor the sources of pollution in these rivers by gathering samples from the primary drains that carry industrial and municipal waste into these water bodies. Furthermore, we aimed to evaluate the impact of PTEs in surface water on groundwater quality by collecting groundwater samples from nearby populated areas. A total of 30 samples were collected from these three sources: rivers (6 samples), drains (9 samples), and groundwater (15 samples). The analysis revealed that the levels of PTEs in the samples from these three resources having a mean value: arsenic (As) 23.5 µg/L, zinc (Zn) 2.35 mg/L, manganese (Mn) 0.51 mg/L, lead (Pb) 6.63 µg/L, and chromium (Cr) 10.9 µg/L, exceeded the recommended values set by the World Health Organization (WHO). Furthermore, PTEs including (As 84%), (Zn 65%), (Mn 69%), (Pb 53%), (Cr 53%), and (Ni 27%), samples were beyond the recommended values of WHO. The results of the Principal Component Analysis indicated that surface water and groundwater exhibited total variability of 83.87% and 85.97%, respectively. This indicates that the aquifers in the study area have been contaminated due to both natural geogenic factors and anthropogenic sources. These sources include the discharge of industrial effluents, wastewater from municipal sources, mining activities, agricultural practices, weathering of rocks, and interactions between rocks and water. Spatial distribution maps clearly illustrated the widespread mobilization of PTEs throughout the study area. Furthermore, a health risk assessment was conducted to evaluate the potential adverse health effects of PTEs through the ingestion of drinking groundwater by both children and adults. Health risk assessment result show the mean carcinogenic values for As, Cr, Pb and Ni in children are calculated to be (1.88E-04), (2.61E-04), (2.16E-02), and (5.74E-05), respectively. Similarly, the mean carcinogenic values for the above mentioned PTEs in adults were recorded to be (2.39E-05), (3.32E-05), (1.19E-03), and (7.29E-06) respectively. The total hazard index values for As, Zn, Cr, Pb, Mn, Cu, and Ni in children were observed to be (9.07E + 00), (9.95E-07), (4.59E-04), (5.75E-04), (4.72E-05), (2.78E-03), and (5.27E-05) respectively. The analysis revealed that As has an adverse effect on the population of the study area as compared to other PTEs investigated in this study.

Application of manganese oxide-based materials for arsenic removal: A review

In the context of growing arsenic (As) contamination in the world, there is an urgent need for an effective treatment approach to remove As from the environment. Industrial wastewater is one of the primary sources of As contamination, which poses significant risks to both microorganisms and human health, as the presence of As can disrupt the vital processes and synthesis of crucial macromolecules in living organisms. The global apprehension regarding As presence in aquatic environments persists as a key environmental issue. This review summarizes the recent advances and progress in the design, strategy, and synthesis method of various manganese-based adsorbent materials for As removal. Occurrence, removal, oxidation mechanism of As(III), As adsorption on manganese oxide (MnOx)-based materials, and influence of co-existing solutes are also discussed. Furthermore, the existing knowledge gaps of MnOx-based adsorbent materials and future research directions are proposed. This review provides a reference for the application of MnOx-based adsorbent materials to As removal.

Efficient performance of InP and InP/ZnS quantum dots for photocatalytic degradation of toxic aquatic pollutants

In recent years, the growing concern over the presence of toxic aquatic pollutants has prompted intensive research into effective and environmentally friendly remediation methods. Photocatalysis using semiconductor quantum dots (QDs) has developed as a promising technology for pollutant degradation. Among various QD materials, indium phosphide (InP) and its hybrid with zinc sulfide (ZnS) have gained considerable attention due to their unique optical and photocatalytic properties. Herein, InP and InP/ZnS QDs were employed for the removal of dyes (crystal violet, and congo red), polyaromatic hydrocarbons (pyrene, naphthalene, and phenanthrene), and pesticides (deltamethrin) in the presence of visible light. The degradation efficiencies of crystal violet (CV) and congo red (CR) were 74.54% and 88.12% with InP, and 84.53% and 91.78% with InP/ZnS, respectively, within 50 min of reaction. The InP/ZnS showed efficient performance for the removal of polyaromatic hydrocarbons (PAHs). For example, the removal percentage for naphthalene, phenanthrene, and pyrene was 99.8%, 99.6%, and 88.97% after the photocatalytic reaction. However, the removal percentage of InP/ZnS for pesticide deltamethrin was 90.2% after 90 min light irradiation. Additionally, advanced characterization techniques including UV-visible spectrophotometer (UV-Vis), photoluminescence (PL), X-ray diffractometer (XRD), energy-dispersive spectrometer (EDS) elemental mapping, transmission electron microscopy (TEM), and thermogravimetric analysis (TGA) were used to analyze the crystal structure, morphology, and purity of the fabricated materials in detail. The particle size results obtained from TEM are in the range of 2.28-4.60 nm. Both materials (InP and InP/ZnS) exhibited a spherical morphology, displaying distinct lattice fringes. XRD results of InP depicted lattice planes (111), (220), and (311) in good agreement with cubic geometry. Furthermore, the addition of dopants was discovered to enhance the thermal stability of the fabricated material. In addition, QDs exhibited efficacy in the breakdown of PAHs. The analysis of their fragmentation suggests that the primary mechanism for PAHs degradation is the phthalic acid pathway.

Fractionation and Characterization of Metallic Elements in Soils in Land Use Systems

Land use has a great impact on soil dynamics. The soils of various land use systems in Central Karakoram have been under immense pressure in the recent past due to certain anthropogenic activities such as land use practices and land use cover changes. These influences have an impact on the spatial distribution of metallic elements (MEs) in the soils of various land uses. Herein, we investigated the occurrence of the MEs, copper (Cu), zinc (Zn), and nickel (Ni), in soils of various land uses such as the permafrost, pasture, forest, and agricultural lands of the Central Karakorum region. The MEs were extracted in exchangeable, adsorbed, organically bound, carbonated, precipitated, and residual forms. The concentrations of MEs showed a significant dependence on the extraction method used, and the extraction trend followed the order of EDTA > HNO3 > KNO3 > NaOH > H2O. Zn showed the highest concentration compared to Ni and Cu in all extractions, whereas the land uses' ME concentration followed the order of agricultural land > permafrost > forest > pasturelands. The highest values of total Zn, Ni, and Cu were 712 ± 01 mg/kg, 656 ± 02 mg/kg, and 163 ± 02 mg/kg, respectively, in agricultural soil. The ME concentration showed significant variations between different land uses, and the highest concentration was noted in agricultural soil. Zn was found to be a dominant ME compared to Ni and Cu. We believe this effort will provide opportunities for scholars to investigate MEs around the globe.

Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 μg/L, with an average of 11.64 μg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 μg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO3- and Na+ are the dominant ions in the study area, and the water types of samples were CaHCO3, mixed CaMgCl, and CaCl, demonstrating that rock-water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F-, Fe2+, K+, Mg2+, Ca2+, Cl-, SO42-, NO3-, HCO3-, and Na+. The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources.

Keeping their own and integrating the other: medicinal plant use among Ormurs and Pathans in South Waziristan, Pakistan

BACKGROUND

In multicultural societies, traditional knowledge among minorities faces several challenges. Minority groups often face difficult situations living in specific peripheral geographies and striving to retain their biocultural heritage, including medicinal plant knowledge and practices. Folk medicinal plant knowledge is a dynamic eco-cultural complex influenced by various environmental, socio-cultural, and political factors. Examining medicinal plant knowledge among minorities has been an increasingly popular topic in cross-cultural ethnobiology. It also helps understand the dynamics of local/traditional ecological knowledge (LEK/TEK) change within a given community. The current study was designed to investigate the status of medicinal plant knowledge among two linguistic groups, i.e. Ormurs and Pathans, living in a remote valley of West Pakistan.

METHODS

We recruited 70 male study participants from the studied groups for semi-structured interviews to record the medicinal plant use of their communities. Data were compared among the two studied communities using the stacked charts employing the presence or absence of data with Past 4.03 and Venn diagrams. Use reports (URs) were counted for each recorded taxon.

RESULTS AND DISCUSSION

A total of seventy-four medicinal plants were quoted as used as ethnomedicines by the researched communities. Most of the reported plants were used to treat digestive and liver problems. The cross-cultural comparison revealed a considerable homogeneity of medicinal plant knowledge (the two groups commonly used more than seventy plants); however, comparing uses recorded for the widely utilised medicinal plants showed numerous idiosyncratic uses among Ormurs but very few among Pathans. Ormurs reported a higher number of cultivated, wild, and imported plant uses than did Pathans. These results indicate that, compared to Pathans, the Ormur linguistic minority retain more folk medicinal plant knowledge, which may be explained by the fact that they have incorporated different folk remedies: their "own knowledge" plus that of Pathans, with whom they have lived together for centuries. Moreover, the local plant nomenclature among Ormurs was highly affected by the plant nomenclature of Pathans.

CONCLUSION

The current study revealed that living together for a few centuries has not implied sharing plant knowledge (as the Pathans do not seem to have learnt from the Ormurs) or, in other words, that plant knowledge exchanges have been unidirectional. The findings show that the Pashto dominant culture may have possibly put pressure on the minority groups and affected local plant-centred cultural practices, as we see in the case of local plant nomenclature hybridisation among Omuri speakers. Hence, it is imperative to employ diverse educational strategies to revitalise the decline of medicinal plant knowledge in the studied communities, especially among Ormurs, who need more attention as they face more challenges than the other group. Locally based strategies should be devised to restore the fading connection with nature, which will be advantageous for revitalising plant knowledge.

DC-AAE: Dual channel adversarial autoencoder with multitask learning for KL-grade classification in knee radiographs

Knee osteoarthritis (OA) is a frequent musculoskeletal disorder that leads to physical disability in older adults. Manual OA assessment is performed via visual inspection, which is highly subjective as it suffers from moderate to high inter-observer variability. Many deep learning-based techniques have been proposed to address this issue. However, owing to the limited amount of labelled data, all existing solutions have limitations in terms of performance or the number of classes. This paper proposes a novel fully automatic Kellgren and Lawrence (KL) grade classification scheme in knee radiographs. We developed a semi-supervised multi-task learning-based approach that enables the exploitation of additional unlabelled data in an unsupervised as well as supervised manner. Specifically, we propose a dual-channel adversarial autoencoder, which is first trained in an unsupervised manner for reconstruction tasks only. To exploit the additional data in a supervised way, we propose a multi-task learning framework by introducing an auxiliary task. In particular, we use leg side identification as an auxiliary task, which allows the use of more datasets, e.g., CHECK dataset. The work demonstrates that the utilization of additional data can improve the primary task of KL-grade classification for which only limited labelled data is available. This semi-supervised learning essentially helps to improve the feature learning ability of our framework, which leads to improved performance for KL-grade classification. We rigorously evaluated our proposed model on the two largest publicly available datasets for various aspects, i.e., overall performance, the effect of additional unlabelled samples and auxiliary tasks, robustness analysis, and ablation study. The proposed model achieved the accuracy, precision, recall, and F1 score of 75.53%, 74.1%, 78.51%, and 75.34%, respectively. Furthermore, the experimental results show that the suggested model not only achieves state-of-the-art performance on two publicly available datasets but also exhibits remarkable robustness.

Integrated approach to hydrogeochemical appraisal of groundwater quality concerning arsenic contamination and its suitability analysis for drinking purposes using water quality index

Arsenic (As), contamination in drinking groundwater resources is commonly environmental problem in many developing countries including Pakistan, with significant human health risk reports. In order to examine the groundwater quality concerning As contamination, its geochemical behavior along with physicochemical parameters, 42 samples were collected from community tube wells from District Bahawalpur, Punjab, Pakistan. The results showed the concentration of elevated As, its source of mobilization, and associated public health risk. The As concentration detected in groundwater samples varied from 0.12 to 104 µg/L with an average value of 34.7 µg/L. Among 42 groundwater samples, 27 samples were beyond the permitted limit of 10 µg/L recommended by World Health Organization (WHO), for drinking purposes. Statistical analysis result show that the groundwater cations values are in decreasing order such as: Na+ > Mg2+ > Ca2+ > K+, while anions were HCO3- > SO42- > Cl- > NO3-. Hydrochemical facies result depict that the groundwater samples of the study area, 14 samples belong to CaHCO3 type, 5 samples belong to NaCl type, 20 samples belong to Mixed CaMgCl type, and 3 samples belong to CaCl2 type. It can be accredited due to weathering and recharge mechanism, evaporation processes, and reverse ion exchange. Gibbs diagram shows that rock water interaction controls the hydrochemistry of groundwater resources of the study area. Saturation Index (SI) result indicated the saturation of calcite, dolomite, gypsum, geothite, and hematite mineral due their positive SI values. The principal component analysis (PCA) results possess a total variability of 80.69% signifying the anthropogenic and geogenic source of contamination. The results of the exposure-health-risk-assessment method for measuring As reveal significant potential non-carcinogenic risk (HQ), exceeding the threshold level of (> 1) for children in the study area. Water quality assessment results shows that 24 samples were not suitable for drinking purposes.

Assessment of heavy metals among auto workers in metropolitan city: a case study

In recent decades, heavy metals (HMs) have emerged as a global health concern. Unfortunately, in Pakistan, there is a general lack of awareness regarding the potential health risks associated with HMs pollution among automobile workers. Herein, we investigated the concentration of heavy metals such as lead (Pb), cadmium (Cd), and chromium (Cr) among automobile workers who were occupationally exposed in Mingora City, Khyber Pakhtunkhwa, Pakistan. Three different automobile groups, i.e., battery recyclers, spray painters, and mechanics were studied in detail. A total of 40 blood samples were collected from automobile workers groups while 10 blood samples were collected as control individuals from different locations in the study area. We investigated heavy metals concentration with a standard method using an atomic absorption spectrometer AAS (PerkinElmer Analyst 700, United States). Based on our findings, the battery recycling group displayed the most elevated Pb levels (5.45 ± 2.11 μg/dL), exceeding those of both the spray painters' group (5.12 ± 1.98 μg/dL) and the mechanics' group (3.79 ± 2.21 μg/dL). This can be attributed to their higher exposure to Pb pollution resulting from the deterioration, dismantling, grinding, or crushing of old batteries. In the context of chromium (Cr) exposure, a similar trend was observed among the battery recycling group, as well as the spray painters and mechanics groups. However, in the case of cadmium (Cd), the mechanics' group exhibited the highest level of exposure (4.45 ± 0.65 μg/dL), surpassing the battery recycling group (1.17 ± 0.45 μg/dL) and the spray painters' group (1.35 ± 0.69 μg/dL), which was attributed to their greater exposure to welding fumes and other activities in their workplace. We believe that our findings will encourage regulatory measures to improve the health of automobile workers. However, further work is needed to determine various health-related issues associated with heavy metal exposure among automobile workers.

Fluoride Contamination in Groundwater of Community Tube Wells, Source Distribution, Associated Health Risk Exposure, and Suitability Analysis for Drinking from Arid Zone

Fluoride (F−) pollution in potable groundwater (GW) is a serious environmental concern in Pakistan with substantial human health hazard reports. The research on F− pollution in GW resources in Sindh Province is still incomplete. To explore the realistic conditions, the present research aimed to investigate the GW quality of community tube wells concerning F− contamination in Tharparkar, Sindh, Pakistan. A total of 53 samples were collected and examined for F−, along with other physicochemical parameters. The F− values observed varied from 0.2–4.2 mg/L, with a mean value of 1.63 mg/L. Among the 53 samples, 46% had F− levels that were higher than the World Health Organization’s (WHO) recommended limit (1.5 mg/L). The water type of the studied region was Ca-HCO3 type, which can be attributed to fresh recharged water. The interaction of rock–water contact controls the hydrochemistry of GW. The GW resources of the research zone were highly saturated with fluorite minerals. Human health risk calculation outcomes exposed that 21 samples showed high HQ values for children and 7 samples showed high values for adults in the research zone. Children are at high risk in the study area from drinking F−-contaminated GW. WQI results showed that 31 samples were not suitable for drinking.

Geochemical control, water quality indexing, source distribution, and potential health risk of fluoride and arsenic in groundwater: Occurrence, sources apportionment, and positive matrix factorization model

Fluoride (F-), and arsenic (As) in the groundwater cause health problems in developing countries, including Pakistan. We evaluated the occurrence, distribution, sources apportionment, and health hazards of F-, and As in the groundwater of Mardan, Pakistan. Therefore, groundwater samples (n = 130) were collected and then analyzed for F-, and As by ion-chromatography (IC) and Inductively-coupled plasma mass-spectrometry (ICP-MS). The F-, and As concentrations in groundwater were 0.7-14.4 mg/L and 0.5-11.2 µg/L. Relatively elevated F-, and As coexists with higher pH, Na+, HCO3-, SO4-2, and depleted Ca+2 due to fluoride, sulfide-bearing minerals, and anthropogenic inputs. Both F-, and/or As are transported in subsurface water through adsorption and desorption processes. Groundwater samples 45%, and 14.2% exceeded the WHO guidelines of 1.5 mg/L and 10 µg/L. Water quality indexing (WQI-model) declared that 35.7% samples are unfit for household purposes. Saturation and undersaturation of minerals showed precipitation and mineral dissolution. Groundwater contamination by PCA-MLR and PMF-model interpreted five factors. The fitting results and R2 values of PMF (0.52-0.99)>PCA-MLR (0.50-0.95) showed high accuracy of PMF-model. Human health risk assessment (HHRA-model) revealed high non-carcinogenic and carcinogenic risk for children than adults. The percentile recovery of F- and As was recorded 98%, and 95% with reproducibility ± 5% error.

Intelligent Framework for Early Detection of Severe Pediatric Diseases from Mild Symptoms

Children's health is one of the most significant fields in medicine. Most diseases that result in children's death or long-term morbidity are caused by preventable and treatable etiologies, and they appear in the child at the early stages as mild symptoms. This research aims to develop a machine learning (ML) framework to detect the severity of disease in children. The proposed framework helps in discriminating children's urgent/severe conditions and notifying parents whether a child needs to visit the emergency room immediately or not. The model considers several variables to detect the severity of cases, which are the symptoms, risk factors (e.g., age), and the child's medical history. The framework is implemented by using nine ML methods. The results achieved show the high performance of the proposed framework in identifying serious pediatric diseases, where decision tree and random forest outperformed the other methods with an accuracy rate of 94%. This shows the reliability of the proposed framework to be used as a pediatric decision-making system for detecting serious pediatric illnesses. The results are promising when compared to recent state-of-the-art studies. The main contribution of this research is to propose a framework that is viable for use by parents when their child suffers from any commonly developed symptoms.

Distribution and Speciation of Trace Elements in Soils of Four Land-Use Systems

Land use has a greater impact on trace element (TE) concentration present in soils. In mountainous regions of the western Himalayas, some dominating geogenic and human-dependent anthropogenic factors are involved in the spatial distribution of TEs in various land uses. Soil samples were collected from permafrost, pasture, forest, and agricultural land-use systems of Babusar Valley and Fairy Meadows in Diamer districts and the Rama region in Astore Districts in replications for investigation of three TEs, i.e., copper (Cu), zinc (Zn), and nickel (Ni). These samples were analyzed for exchangeable, adsorbed, organically bound, carbonate precipitated, and residual forms. Significant differences among these TEs were observed. Differences in the levels of TEs within soil samples were observed to be influenced by land usage patterns. The physicochemical properties of soil samples were also investigated. Additionally, the total metals (Ni, Zn, Cu) were extracted and their concentrations were measured in all samples. The concentration of soil TEs was observed in the following order: adsorbed < organically bound < exchangeable < residual < carbonate precipitated form across all the land uses. The results indicate that the contents of TEs (Ni, Zn, Cu) in agricultural soils were greater than in the permafrost pasture and forest soil samples. The total TE concentration varied as Zn > Ni > Cu irrespective of the area and land uses. We believe this work will open avenues for researchers to explore TEs in various regions of the world.

Antimicrobial efficacy of Mentha piperata-derived biogenic zinc oxide nanoparticles against UTI-resistant pathogens

Misuse of antibiotics leads to the worldwide spread of antibiotic resistance, which motivates scientists to create new antibiotics. The recurring UTI due to antibiotics-resistant microorganism's challenges scientists globally. The biogenic nanoparticles have the potential to meet the escalating requirements of novel antimicrobial agents. The green synthesis of nanoparticles (NPs) gained more attention due to their reliable applications against resistant microbes. The current study evaluates the biogenic ZnO NPs of Mentha piperata extract against resistant pathogens of urinary tract infections by agar well diffusion assay. The biogenic ZnO NPs revealed comparatively maximum inhibition in comparison to synthetic antibiotics against two bacterial strains (Proteus mirabilis, Pseudomonas aeruginosa) and a fungal strain (Candida albicans).The synthesized biogenic ZnO NPs alone revealed maximum activities than the combination of plant extract (PE) and ZnO NPs, and PE alone. The physiochemical features of ZnO NPs characterized through UV-Vis spectroscopy, FTIR, XRD, SEM, and EDX. The UV-Vis spectroscopy revealed 281.85 nm wavelengths; the XRD pattern revealed the crystalline structure of ZnO NPs. The FTIR analysis revealed the presence of carboxylic and nitro groups, which could be attributed to plant extract. SEM analysis revealed spherical hollow symmetry due to electrostatic forces. The analysis via EDX confirmed the presence of Zn and oxygen in the sample. The physiochemical features of synthesized ZnO NPs provide pivotal information such as quality and effectiveness. The current study revealed excellent dose-dependent antimicrobial activity against the pathogenic isolates from UTI-resistant patients. The higher concentration of ZnONPs interacts with the cell membrane which triggers oxidative burst. They may bind with the enzymes and proteins and brings epigenetic alteration which leads to membrane disruption or cell death.

TP53 and its Regulatory Genes as Prognosis of Cutaneous Melanoma

The present study was the first comprehensive investigation of genetic mutation and expression levels of the p53 signaling genes in cutaneous melanoma through various genetic databases providing large datasets. The mutational landscape of p53 and its signaling genes was higher than expected, with TP53 followed by CDKN2A being the most mutated gene in cutaneous melanoma. Furthermore, the expression analysis showed that TP53, MDM2, CDKN2A, and TP53BP1 were overexpressed, while MDM4 and CDKN2B were under-expressed in cutaneous melanoma. Overall, TCGA data revealed that among all the other p53 signaling proteins, CDKN2A was significantly higher in both sun and non-sun-exposed healthy tissues than in melanoma. Likewise, MDM4 and TP53BP1 expressions were markedly greater in non-sun-exposed healthy tissues compared to other groups. However, CDKN2B expression was higher in the sun-exposed healthy tissues than in other tissues. In addition, various genes were expressed significantly differently among males and females. In addition, CDKN2A was highly expressed in the SK-MEL-30 skin cancer cell line, whereas, Immune cell type expression analysis revealed that the MDM4 was highly expressed in naïve B-cells. Furthermore, all six genes were significantly overexpressed in extraordinarily overweight or obese tumor tissues compared to healthy tissues. MDM2 expression and tumor stage were closely related. There were differences in gene expression across patient age groups and positive nodal status. TP53 showed a positive correlation with B cells, MDM2 with CD8+T cells, macrophages and neutrophils, and MDM4 with neutrophils. CDKN2A/B had a non-significant correlation with all six types of immune cells. However, TP53BP1 was positively correlated with all five types of immune cells except B cells. Only TP53, MDM2, and CDKN2A had a role in cutaneous melanoma-specific tumor immunity. All TP53 and its regulating genes may be predictive for prognosis. The results of the present study need to be validated through future screening, in vivo, and in vitro studies.

Antibacterial Efficacy of Green Synthesized Silver Nanoparticles Using Salvia nubicola Extract against Ralstonia solanacearum, the Causal Agent of Vascular Wilt of Tomato

Ralstonia solanacearum is a phytopathogen causing bacterial wilt diseases of tomato and affecting its productivity, which leads to prominent economic losses annually. As an alternative to conventional pesticides, green synthesized nanoparticles are believed to possess strong antibacterial activities besides being cheap and ecofriendly. Here, we present the synthesis of silver nanoparticles (Sn-AgNPs) from medicinally important aqueous plant extracts of Salvia nubicola. Characterization of biologically synthesized nanoparticles was performed through UV-vis spectrophotometry, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy (TEM), and thermogravimetric analysis. The antibacterial activity of the biosynthesized silver nanoparticles was tested against the phytopathogen R. solanacearum through in vitro experiments. Preliminary phytochemical analysis of the plant extracts revealed the presence of substantial amounts of flavonoids (57.08 mg GAE/g), phenolics (42.30 mg GAE/g), tannins, and terpenoids. The HPLC phenolic profile indicated the presence of 25 possible bioactive compounds. Results regarding green synthesized silver nanoparticles revealed the conformation of different functional groups through FTIR analysis, which could be responsible for the bioreduction and capping of Ag ions into silver NPs. TEM results revealed the spherical, crystalline shape of nanoparticles with the size in the range of 23-63 nm, which validates SEM results. Different concentrations of Sn-AgNPs (T1 (500 μg/mL) to T7 (78.1 μg/mL)) with a combination of plant extracts (PE-Sn-AgNPs) and plant extracts alone exhibited an efficient inhibition of R. solanacearum. These findings could be used as an effective alternative preparation against the bacterial wilt of tomato.

Comparative Effects of Hydropriming and Iron Priming on Germination and Seedling Morphophysiological Attributes of Stay-Green Wheat

Seed priming is considered to play an essential role in the overall improvement of agricultural crops. The current research work was carried out in order to investigate the comparative effects of hydropriming and iron priming on the germination behavior and morphophysiological attributes of wheat seedlings. The experimental materials consisted of three wheat genotypes including a synthetically derived wheat line (SD-194), stay-green wheat genotype (Chirya-7), and conventional wheat variety (Chakwal-50). The treatments included hydro (distilled and tap water)- and iron priming (10 and 50 mM) of wheat seeds for 12 h duration. Results indicated that both priming treatment and wheat genotypes exhibited highly different germination and seedling characteristics. These included germination percentage, root volume, root surface, root length, relative water content, chlorophyll content, membrane stability index, and chlorophyll fluorescence attributes. Furthermore, the synthetically derived line (SD-194) was the most promising in majority of the studied attributes by exhibiting a high germination index (2.21%), root fresh weight (7.76%), shoot dry weight (3.36%), relative water content (19.9%), chlorophyll content (7.58%), and photochemical quenching coefficient (2.58%) when compared with stay-green wheat (Chirya-7). The study also found that hydropriming with tap water and priming wheat seeds with low concentrations of iron yielded better results when a comparison was made with wheat seeds primed at high concentrations of iron. Therefore, wheat seed priming with tap water and iron solution for 12 h is recommended for optimum wheat improvement. Furthermore, current findings suggest that seed priming may have the prospect of an innovative and user-friendly approach for wheat biofortification with the aim of enhanced iron acquisition and accumulation in grains.

Biochemical and phenological characterization of diverse wheats and their association with drought tolerance genes

Drought is one of the most important wheat production limiting factor, and can lead to severe yield losses. This study was designed to examine the effect of drought stress on wheat physiology and morphology under three different field capacities (FC) viz. 80% (control), 50% (moderate) and 30% (severe drought stress) in a diverse collection of wheat germplasm including cultivars, landraces, synthetic hexaploid and their derivatives. Traits like grain weight, thousand grain weight and biomass were reduced by 38.23%, 18.91% and 26.47% respectively at 30% FC, whereas the reduction rate for these traits at 50% FC were 19.57%, 8.88% and 18.68%. In principal component analysis (PCA), the first two components PC1 and PC2 accounted for 58.63% of the total variation and separated the cultivars and landraces from synthetic-based germplasm. Landraces showed wide range of phenotypic variations at 30% FC compared to synthetic-based germplasm and improved cultivars. However, least reduction in grain weight was observed in improved cultivars which indicated the progress in developing drought resilient cultivars. Allelic variations of the drought-related genes including TaSnRK2.9-5A, TaLTPs-11, TaLTPs-12, TaSAP-7B-, TaPPH-13, Dreb-B1 and 1fehw3 were significantly associated with the phenological traits under drought stress in all 91 wheats including 40 landraces, 9 varieties, 34 synthetic hexaploids and 8 synthetic derivatives. The favorable haplotypes of 1fehw3, Dreb-B1, TaLTPs-11 and TaLTPs-12 increased grain weight, and biomass. Our results iterated the fact that landraces could be promising source to deploy drought adaptability in wheat breeding. The study further identified drought tolerant wheat genetic resources across various backgrounds and identified favourable haplotypes of water-saving genes which should be considered to develop drought tolerant varieties.

SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation

Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.

Unveiling Lathyrus aphaca L. as a Newly Identified Host for Begomovirus Infection: A Comprehensive Study

The Begomovirus genus of the family Geminiviridae comprises the largest group of geminiviruses. Begomoviruses are transmitted by the whitefly complex (Bemisia tabaci) and infect dicotyledonous plants in tropical and subtropical regions. The list of begomoviruses is continuously increasing as a result of improvements in the methods for identification, especially from weed plants, which are considered a source of new viruses and reservoirs of economically important viruses but are often neglected during diversity studies. Lathyrus aphaca L. weed plants (yellow-flowered pea) with varicose veins and discoloration of the leaves were found. Amplified genomic DNA through rolling circular amplification was subjected to PCR analysis for the detection of the viral genome and associated DNA-satellites (alphasatellites and betasatellites). A full-length sequence (2.8 kb) of a monopartite begomovirus clone was determined; however, we could not find any associated DNA satellites. The amplified full-length clone of Rose leaf curl virus (RoLCuV) reserved all the characteristics and features of an Old World (OW) monopartite begomovirus. Furthermore, it is the first time it has been reported from a new weed host, yellow-flowered pea. Rolling circle amplification and polymerase chain reaction analysis of associated DNA satellites, alphasatellite, and betasatellite, were frequently accomplished but unable to amplify from the begomovirus-infected samples, indicating the presence of only monopartite Old World begomovirus. It is observed that RoLCuV has the capability to infect different hosts individually without the assistance of any DNA satellite component. Recombination in viruses is also a source of begomovirus infection in different hosts.

Groundwater fluoride and nitrate contamination and associated human health risk assessment in South Punjab, Pakistan

Consumption of high fluoride (F^-) and nitrate (NO₃^-) containing water may pose serious health hazards. One hundred sixty-one groundwater samples were collected from drinking wells in Khushab district, Punjab Province, Pakistan, to determine the causes of elevated F^- and NO₃^- concentrations, and to estimate the human health risks posed by groundwater contamination. The results showed pH of the groundwater samples ranged from slightly neutral to alkaline, and Na⁺ and HCO₃^- ions dominated the groundwater. Piper diagram and bivariate plots indicated that the key factors regulating groundwater hydrochemistry were weathering of silicates, dissolution of evaporates, evaporation, cation exchange, and anthropogenic activities. The F^- content of groundwater ranged from 0.06 to 7.9 mg/L, and 25.46% of groundwater samples contained high-level fluoride concentration (F^-  > 1.5 mg/L), which exceeds the (WHO Guidelines for drinking-water quality: incorporating the first and second addenda, WHO, Geneva, 2022) guidelines of drinking-water quality. Inverse geochemical modeling indicates that weathering and dissolution of fluoride-rich minerals were the primary causes of F^- in groundwater. High F^- can be attributed to low concentration of calcium-containing minerals along the flow path. The concentrations of NO₃^- in groundwater varied from 0.1 to 70 mg/L; some samples are slightly exceeding the (WHO Guidelines for drinking-water quality: incorporating the first and second addenda, WHO, Geneva, 2022) guidelines for drinking-water quality. Elevated NO₃^- content was attributed to the anthropogenic activities revealed by PCA analysis. The high levels of nitrates found in the study region are a result of various human-caused factors, including leaks from septic systems, the use of nitrogen-rich fertilizers, and waste from households, farming operations, and livestock. The hazard quotient (HQ) and total hazard index (THI) of F^- and NO₃^- showed high non-carcinogenic risk (> 1) via groundwater consumption, demonstrating a high potential risk to the local population. This study is significant because it is the most comprehensive examination of water quality, groundwater hydrogeochemistry, and health risk assessment in the Khushab district to date, and it will serve as a baseline for future studies. Some sustainable measures are urgent to reduce the F^- and NO₃^- content in the groundwater.

Water stress effects on stay green and chlorophyll fluorescence with focus on yield characteristics of diverse bread wheats

MAIN CONCLUSION

The study provided an insight toward better understanding of stay-green mechanisms for drought tolerance improvement and identified that synthetic-derived wheats proved as a promising germplasm for improved tolerance against water stress. Stay-green (SG) trait is considered to be related with the ability of wheat plants to maintain photosynthesis and CO₂ assimilation. The present study explored the interaction of water stress with SG expression through physio-biochemical, agronomic and phenotypic responses among diverse wheat germplasm comprising of 200 synthetic hexaploids, 12 synthetic derivatives, 97 landraces and 16 conventional bread wheat varieties, for 2 years. The study established that variation of SG trait existed in the studied wheat germplasm and there was positive association between SG trait and tolerance to water stress. The relationship of SG trait with chlorophyll content (r = 0.97), ETR (r = 0.28), GNS (r = 0.44), BMP (r = 0.34) and GYP (r = 0.44) was particularly promising under water stress environment. Regarding chlorophyll fluorescence, the positive correlation of фPSII (r = 0.21), qP (r = 0.27) and ETR (r = 0.44) with grain yield per plant was noted. The improved ΦPSII and Fv/Fm of PSII photochemistry resulted in the high photosynthesis activity in SG wheat genotypes. Regarding relative water content and photochemical quenching coefficient, synthetic-derived wheats were better by maintaining 20.9, 9.8 and 16.1% more RWC and exhibiting 30.2, 13.5 and 17.9% more qP when compared with landraces, varieties and synthetic hexaploids, respectively, under water stress environment. Synthetic derived wheats also exhibited relatively more SG character with good yield and were more tolerant to water stress in terms of grain yield, grain weight per plant, better photosynthetic performance through chlorophyll fluorescence measurement, high leaf chlorophyll and proline content, and hence, may be used as novel sources for breeding drought tolerant materials. The study will further facilitate research on wheat leaf senescence and will add to better understanding of SG mechanisms for drought tolerance improvement.

Traditional Uses of Wild Edible Mushrooms among the Local Communities of Swat, Pakistan

Mushrooms play a crucial role in human life as well as in nature, providing food, medicine, and carrying out vital processes of decomposition, nutrient recycling, and developing mycorrhizal association with plants. The traditional system of knowledge about identification, collection, and usage of mushrooms has been accumulated through the shared experiences of many generations. Unfortunately, there have been continuous threats to the folk knowledge of mushrooms mainly due to habitat degradation, urbanization, and contemporary medication. The current research was, therefore, aimed to document an ethnomycological knowledge possessed by the ethnic communities of Swat, Pakistan. The purposive randomized sampling was carried out using chain referral method. Ethno-mycological information was collected from 62 informants using free listing, preference ranking, and use totaled methods. In total, 34 species of mushrooms belonging to 31 genera and 21 families were reported. About 85% of the reported species belong to Basidiomycetes, and 12.5% to Ascomycetes are used as food and for medicinal purposes. Morchella angusticeps, M. esculenta, Pleurotus sp., Auricularia sp., Flammulina velutipes, Agaricus bisporus, Ganoderma lucidum, and Sanghuangporus sanghuang were among the most cited edible and medicinal mushrooms. The current study revealed that district Swat is rich in wild edible and medicinal mushrooms (WEMs), and the local communities possess rich traditional knowledge about their collection, storage, and utilization. The diversity of WEMs of this region could contribute substantially to the socio-economic uplifting of the local communities through appropriate domestication and commercialization. Anthropogenic factors, coupled with depletion of traditional knowledge, threaten the diversity of WEMs in the region; therefore, in situ and ex situ conservation strategies are highly recommended.

MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images

Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.

Asymmetric Alternative Current Electrochemical Method Coupled with Amidoxime-Functionalized Carbon Felt Electrode for Fast and Efficient Removal of Hexavalent Chromium from Wastewater

A large amount of Cr (VI)-polluted wastewater produced in electroplating, dyeing and tanning industries seriously threatens water ecological security and human health. Due to the lack of high-performance electrodes and the coulomb repulsion between hexavalent chromium anion and cathode, the traditional DC-mediated electrochemical remediation technology possesses low Cr (VI) removal efficiency. Herein, by modifying commercial carbon felt (O-CF) with amidoxime groups, amidoxime-functionalized carbon felt electrodes (Ami-CF) with high adsorption affinity for Cr (VI) were prepared. Based on Ami-CF, an electrochemical flow-through system powered by asymmetric AC was constructed. The mechanism and influencing factors of efficient removal of Cr (VI) contaminated wastewater by an asymmetric AC electrochemical method coupling Ami-CF were studied. Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and X-ray photoelectron spectroscopy (XPS) characterization results showed that Ami-CF was successfully and uniformly loaded with amidoxime functional groups, and the adsorption capacity of Cr (VI) was more than 100 times higher than that of O-CF. In particular, the Coulomb repulsion effect and the side reaction of electrolytic water splitting were inhibited by the high-frequency anode and cathode switching (asymmetric AC), the mass transfer rate of Cr (VI) from electrode solution was increased, the reduction efficiency of Cr (VI) to Cr (III) was significantly promoted and a highly efficient removal of Cr (VI) was achieved. Under optimal operating conditions (positive bias 1 V, negative bias 2.5 V, duty ratio 20%, frequency 400 Hz, solution pH = 2), the asymmetric AC electrochemistry based on Ami-CF can achieve fast (30 s) and efficient removal (>99.11%) for 0.5-100 mg·L^-1 Cr (VI) with a high flux of 300 L h^-1 m^-2. At the same time, the durability test verified the sustainability of the AC electrochemical method. For Cr (VI)-polluted wastewater with an initial concentration of 50 mg·L^-1, the effluent concentration could still reach drinking water grade (<0.05 mg·L^-1) after 10 cycling experiments. This study provides an innovative approach for the rapid, green and efficient removal of Cr (VI) containing wastewater at low and medium concentrations.

Residual attention based uncertainty-guided mean teacher model for semi-supervised breast masses segmentation in 2D ultrasonography

Breast tumor is the second deadliest disease among women around the world. Earlier tumor diagnosis is extremely important for improving the survival rate. Recent deep-learning techniques proved helpful in the timely diagnosis of various tumors. However, in the case of breast tumors, the characteristics of the tumors, i.e., low visual contrast, unclear boundary, and diversity in shape and size of breast lesions, make it more challenging to design a highly efficient detection system. Additionally, the scarcity of publicly available labeled data is also a major hurdle in the development of highly accurate and robust deep-learning models for breast tumor detection. To overcome these issues, we propose residual-attention-based uncertainty-guided mean teacher framework which incorporates the residual and attention blocks. The residual for optimizing the deep network by enabling the flow of high-level features and attention modules improves the focus of the model by optimizing its weights during the learning process. We further explore the potential of utilizing unlabeled data during the training process by employing the semi-supervised learning (SSL) method. Particularly, the uncertainty-guided mean-teacher student architecture is exploited to demonstrate the potential of incorporating the unlabeled samples during the training of residual attention U-Net model. The proposed SSL framework has been rigorously evaluated on two publicly available labeled datasets, i.e., BUSI and UDIAT datasets. The quantitative as well as qualitative results demonstrate that the proposed framework achieved competitive performance with respect to the previous state-of-the-art techniques and outperform the existing breast ultrasound masses segmentation techniques. Most importantly, the study demonstrates the potential of incorporating the additional unlabeled data for improving the performance of breast tumor segmentation.

A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques

With the rise of the Internet and social media, information has become available at our fingertips. However, on the dark side, these advancements have opened doors for fraudsters. Online recruitment fraud (ORF) is one of the problems created by these modern technologies, as hundreds of thousands of applicants are victimized every year globally. Fraudsters advertise bogus jobs on online platforms and target job hunters with fake offerings such as huge salaries and desirable geographical locations. The objective of these fraudsters is to collect personal information to be misused in the future, leading to the loss of applicants' privacy. To prevent such situations, there is a need for an automatic detecting system that can distinguish between real and fake job advertisements and preserve the applicants' privacy. This study attempts to build a smart secured framework for detecting and preventing ORF using ensemble machine learning (ML) techniques. In this regard, four ensemble methods-AdaBoost (AB), Xtreme Gradient Boost (XGB), Voting, and Random Forest (RF)-are used to build a detection framework. The dataset used was pre-processed using several methods for cleaning and denoising in order to achieve better outcomes. The performance evaluation measures of the applied methods were accuracy, precision, sensitivity, F-measure, and ROC curves. According to these measures, AB performed best, followed by XGB, voting, and RF. In the proposed framework, AB achieved a high accuracy of 98.374%, showing its reliability for detecting and preventing ORF. The results of AB were compared to existing methods in the literature validating the reliability of the model to be significantly used for detecting ORF.

Groundwater Quality, Health Risk Assessment, and Source Distribution of Heavy Metals Contamination around Chromite Mines: Application of GIS, Sustainable Groundwater Management, Geostatistics, PCAMLR, and PMF Receptor Model

Groundwater contamination by heavy metals (HMs) released by weathering and mineral dissolution of granite, gneisses, ultramafic, and basaltic rock composition causes human health concerns worldwide. This paper evaluated the heavy metals (HMs) concentrations and physicochemical variables of groundwater around enriched chromite mines of Malakand, Pakistan, with particular emphasis on water quality, hydro-geochemistry, spatial distribution, geochemical speciation, and human health impacts. To better understand the groundwater hydrogeochemical profile and HMs enrichment, groundwater samples were collected from the mining region (n = 35), non-mining region (n = 20), and chromite mines water (n = 5) and then analyzed using ICPMS (Agilent 7500 ICPMS). The ranges of concentrations in the mining, non-mining, and chromite mines water were 0.02-4.5, 0.02-2.3, and 5.8-6.0 mg/L for CR, 0.4-3.8, 0.05-3.6, and 3.2-5.8 mg/L for Ni, and 0.05-0.8, 0.05-0.8, and 0.6-1.2 mg/L for Mn. Geochemical speciation of groundwater variables such as OH^-, H⁺, Cr⁺², Cr⁺³, Cr⁺⁶, Ni⁺², Mn⁺², and Mn⁺³ was assessed by atomic fluorescence spectrometry (AFS). Geochemical speciation determined the mobilization, reactivity, and toxicity of HMs in complex groundwater systems. Groundwater facies showed 45% CaHCO₃, 30% NaHCO₃, 23.4% NaCl, and 1.6% Ca-Mg-Cl water types. The noncarcinogenic and carcinogenic risk of HMs outlined via hazard quotient (HQ) and total hazard indices (THI) showed the following order: Ni > Cr > Mn. Thus, the HHRA model suggested that children are more vulnerable to HMs toxicity than adults. Hierarchical agglomerative cluster analysis (HACA) showed three distinct clusters, namely the least, moderately, and severely polluted clusters, which determined the severity of HMs contamination to be 66.67% overall. The PCAMLR and PMF receptor model suggested geogenic (minerals prospects), anthropogenic (industrial waste and chromite mining practices), and mixed (geogenic and anthropogenic) sources for groundwater contamination. The mineral phases of groundwater suggested saturation and undersaturation. Nemerow's pollution index (NPI) values determined the unsuitability of groundwater for domestic purposes. The EC, turbidity, PO₄^-3, Na⁺, Mg⁺², Ca⁺², Cr, Ni, and Mn exceeded the guidelines suggested by the World Health Organization (WHO). The HMs contamination and carcinogenic and non-carcinogenic health impacts of HMs showed that the groundwater is extremely unfit for drinking, agriculture, and domestic demands. Therefore, groundwater wells around the mining region need remedial measures. Thus, to overcome the enrichment of HMs in groundwater sources, sustainable management plans are needed to reduce health risks and ensure health safety.

Densely attention mechanism based network for COVID-19 detection in chest X-rays

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.

Morphological characterization, hazardous metal contamination, source identification, and health risk assessment of the fine road dust from Dachang mining area, China

Dachang mining area in China is known as "paradise for mineralogists" due to its most reserves of Sn, Sb, Pb, and Zn non-ferrous metal resources; thus, its evaluation for heavy metal assessment and consequent health risk is unavoidable. Sixty road dust samples were collected from study area to explore pollution level, ecological, and health risks from heavy metals and were analyzed by an inductively coupled plasma optical emission spectrometer and atomic fluorescence spectrometer. The results showed that average concentration of all the heavy metals in road dust in both mining and residential areas were remarkable higher than its corresponding background values, the former being more severe, except for Cr and Co. The morphological investigation showed that most of the particles were much less than 100 μm illustrating fine part of the road dust samples. Based on integrated pollution indices, Cd, Sb, As, Zn, and Pb were extremely contaminated and exceeded hundred times of the maximum risk value. The health risk assessment revealed substantially higher carcinogenic and non-carcinogenic risks to children and adult. Highest non-carcinogenic risk resulted from arsenic in mining and residential area with HQ_ing of 644.56 and 267.94 respectively (standard HQ ≥ 1) while carcinogenic risk to children (1.94E + 00) which greatly exceeded from the threshold value of (1.0E-4). Sb, Cd, and Pb also posed carcinogenic and non-carcinogenic risk in road dust which is caused by excessive mining activities and heavy vehicle movement in the study area.

Arsenic Contamination, Water Toxicity, Source Apportionment, and Potential Health Risk in Groundwater of Jhelum Basin, Punjab, Pakistan

Potable groundwater (GW) contamination through arsenic (As) is a commonly reported environmental issue in Pakistan. In order to examine the groundwater quality for As contamination, its geochemical behavior, and other physicochemical parameters, 69 samples from various groundwater sources were collected from the mining area of Pind Dadan Khan, Punjab, Pakistan. The results showed the concentration of elevated As, its source of mobilization, and linked public health risk. Arsenic detected in the groundwater samples varied from 0.5 to 100 µg/L, with an average value of 21.38 µg/L. Forty-two samples were beyond the acceptable limit of 10 µg/L of the WHO for drinking purposes. The statistical summary showed that the groundwater cation concentration was in decreasing order such as Na⁺  > Ca²⁺  > Mg²⁺ > K⁺, while anions were as follows: HCO₃^- > SO4^2- > Cl^- > NO₃^-. Hydrochemical facies results depicted that groundwater samples belong to CaHCO₃ type. Rock-water interactions control the hydrochemistry of groundwater. Saturation indices' results indicated the saturation of the groundwater sources for CO₃ minerals due to their positive SI values. Such minerals include aragonite, calcite, dolomite, and fluorite. The principal component analysis (PCA) findings possess a total variability of 77.36% suggesting the anthropogenic and geogenic contributing sources of contaminant. The results of the Exposure-health-risk-assessment model for measuring As reveal significant potential carcinogenic risk exceeding the threshold level (value > 10^-4) and HQ level (value > 1.0).

Occurrence and Distribution of Heavy Metals in Mining Degraded Soil and Medicinal Plants: A Case Study of Pb/Zn Sulfide Terrain Northern Areas, Pakistan

Mining activities have serious environmental impacts, thus releasing heavy metals (HMs) such as cadmium (Cd), lead (Pb), chromium (Cr), zinc (Zn) and nickel (Ni) into the surrounding environment. The current paper investigated the impacts of mining activities of Pb-Zn sulfide on soil and medicinal plants. Hence, soil samples (n = 36) and medicinal plants (n = 36) samples were collected, acid extracted and were analyzed through Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for HMs quantification. Our results showed that mineralized zones showed high HMs enrichment levels as compared to non-mineralized zones. Various Indices for HMs assessment revealed that the contaminated soil of the study area had low to extreme level. The mean concentrations of HMs in mining degraded soil and medicinal plants were significantly higher (p ≤ 0.01) and were found in order of Zn > Pb > Cr > Ni > Cd and Cr > Zn > Pb > Ni > Cd respectively. Similarly, some widely consumable medicinal plants showed good metal accumulation for Cd, Cr and Pb i.e., 3.57 mg kg¹, 350 mg kg^-1 and 335 mg kg^-1 in C. sativa, while 5.9 mg kg-1, 276.9 mg kg-1 and 188.7 mg kg-1 in R. hestatus respectively. Hence, the present study recommended that medicinal plants grown in mining areas should be analyzed for HMs concentration before consumption.

Stochastic adaptive-service level agreement-based energy management model for smart grid and prosumers

The growing issue of demand-supply management between the prosumers and the local energy market requires an efficient and reliable energy management model. The microlayers, such as prosumers, energy districts, and macro players, namely retail dealers and wholesale dealers play a pivotal role in achieving mutual benefits. The stochastic nature of renewable energy generation in energy districts requires an effective model that can contemplate all stochastic complexities. Therefore, this paper proposes a mutual trade model between energy districts and smart grid to authorize the prosumers for mutual energy transactions under the stochastic adaptive-service level agreement. Moreover, multiple smart contacts are developed between the stakeholders to design adaptability and stochastic behavior of wind speed and solar irradiance. The real-time adaptations of the stochastic adaptive-service level agreement are based on technical beneficial feasibility and achieved through stochastic and adaptive functions. The optimized solution based on a genetic algorithm is proposed for the energy cost and energy surplus of prosumers and output parameters of the mutual trade model (grid revenue). In the context of mutual benefits associated with balanced demand and supply, the economic load dispatch and simplex method maximization are used for optimized demand-supply energy management. Moreover, the effectiveness of the proposed adaptive and stochastic mutual trade model is validated through simulation and statistical analysis.

Evaluating a Hybrid Circuit Topology for Fault-Ride through in DFIG-Based Wind Turbines

Large-scale wind power integration has raised concerns about the reliability and stability of power systems. The rotor circuit of a doubly fed induction generator (DFIG) is highly vulnerable to unexpected voltage dips, which can cause considerable electromotive force in the circuit. Consequently, the DFIG must fulfil the fault-ride through (FRT) criteria to ensure the system's performance and contribute to voltage regulation during severe grid outages. This paper provides a hybrid solution for DFIG wind turbines with FRT capabilities, using both a modified switch-type fault current limiter (MSFTCL) and a direct current (DC) chopper. The proposed system has the merit of keeping the rotor current and the DC-link voltage within the permissible limits, enhancing the FRT capability of generators. Moreover, the boundness of supply voltage into its reference value ensures dynamic stability during symmetric and asymmetric grid failures. Further, electromagnetic torque variations are significantly reduced during fault events. Finally, the performance validation of the proposed scheme is performed in a simulation setup, and the results are compared with the existing sliding mode control (SMC) and proportional-integral (PI) controller-based approaches. The comparison results show that a hybrid strategy with advanced controllers provides superior performance for all critical parameters.

Corrigendum to "Pharmacognostic evaluation of Artemisia maritima L. a highly medicinal specie of genus Artemisia" [Saudi J. Biol. Sci. 29(10) (2022) 103419]

[This corrects the article DOI: 10.1016/j.sjbs.2022.103419.].

Plant Use Adaptation in Pamir: Sarikoli Foraging in the Wakhan Area, Northern Pakistan

The study recorded the food uses of wild food plants (WFPs) among the Sarikoli diaspora and the dominant Wakhi in Broghil Valley, North Pakistan, to understand their food adaptation, mainly by looking through the lens of food ethnobotanies. A total of 30 participants took part in the study, which included 15 elderly individuals from each ethnic group. Data were gathered through semi-structured interviews. We recorded 29 WFPs, mostly used as cooked vegetables and snacks. The food uses, as well as the local plant nomenclatures, linked to WFPs of the two studied groups were completely homogenized, which could be attributed to the cultural assimilation of the Sarikoli people to Wakhi culture. We found that although traditional knowledge on WFPs has been homogenized, social change in nearby regions is also threatening the traditional knowledge of the two communities, as evidenced by the smaller number of plants reported compared to that of all other field ethnobotanical studies conducted in nearby regions. Moreover, the growth of legal restrictions and sanctions on accessing natural resources are posing serious challenges to cultural resilience in the valley, and the restrictions on cross-border movement in particular are creating challenges for those who have cross-border kinship relationships between the two groups. We suggest specific measures, such as the promotion of food tourism and educational activities, to protect traditional knowledge and bicultural heritage from further erosion in the region.

Wild Vicia Species Possess a Drought Tolerance System for Faba Bean Improvement

Faba bean (Vicia faba L.), a drought-sensitive crop, is drastically affected by drought stresses compromising its growth and yield. However, wild relatives of faba bean are considered a reservoir of potential genetic resources for tolerance against abiotic stresses. This study was conducted to characterize wild relatives of faba bean for identification of a specific tolerance system required for its improvement against drought stress. The study focused on physiological, biochemical, and anatomical responses of wild Vicia species under drought stress conditions. The experiment was carried out under various levels of drought stress imposed through different field capacities (FC) which included 80% FC ie (well-watered condition), 55% FC (moderate stress), and 30% FC (severe stress). When compared to plants grown in a control environment, drought stress significantly reduced the studied physiological attributes including soluble sugars (21.3% and 15.8%), protein contents (14.7 and 14.6%), and chlorophyll (8.4 and 28.6%) under moderate (55% FC) and severe drought stress (30% FC), respectively. However, proline content increased by 20.5% and 27.6%, peroxidase activity by 48.5% and 57.1%, and superoxide dismutase activity by 72.6% and 64.8% under moderate and severe stress, respectively. The studied anatomical attributes were also affected under drought stress treatments, including diameter of stem xylem vessels (9.1% and 13.7%), leaf lower epidermal thickness (8.05% and 13.34%), and leaf phloem width (5.3% and 10.1%) under moderate and severe stress, respectively. Wild Vicia spp. showed better tolerance to water-deficit conditions as compared to cultivated Vicia L. The observed potential diversity for drought tolerance in wild Vicia spp. may assist in improvement of faba bean and may also help in understanding the mechanisms of adaptations in drought-prone environments.

Exploiting the drought tolerance of wild Elymus species for bread wheat improvement

Crop wild resources are excellent sources of new genetic variation for resilience against climate extremes. However, detailed characterization of the desirable phenotypes is essential before using these crop wild resources in breeding programs. This current study was, therefore, conducted to investigate the water stress responses of eight wild Elymus species and two wheat cultivars. The experiment was carried out under varying levels of osmotic stress induced by polyethylene glycol and progressive water stress through different field capacities. Water stress significantly reduced both physiological and biochemical traits compared to control, ranging from 7.1% (protein content) to 34.5% (chlorophyll) under moderate stress and 9.1-45.8% under severe stress. The anatomical features were also affected under progressive water stress, including a reduction in xylem vessel diameter (7.92 and 16.50%), phloem length (4.36 and 7.18%), vascular bundle length (3.09 and 6.04%), and ground tissue thickness (2.36 and 5.52%), respectively. Conclusively, Elymus borianus (endemic to Swat, Pakistan), E. russelli, E. caninus, E. longioristatus, and E. dauhuricus outperformed the check wheat cultivar, Pirsabak 2005, which is a rainfed variety. The results revealed that Elymus species belonging to the tertiary gene pool of bread wheat could be an excellent drought tolerance source for use in a breeding program.

Hydrogeochemical assessment of carcinogenic and non-carcinogenic health risks of potentially toxic elements in aquifers of the Hindukush ranges, Pakistan: insights from groundwater pollution indexing, GIS-based, and multivariate statistical approaches

Globally, potentially toxic elements (PTEs) and bacterial contamination pose health hazards, persistency, and genotoxicity in the groundwater aquifer. This study evaluates PTE concentration, carcinogenic and noncarcinogenic health hazards, groundwater quality indexing (GWQI-model), source provenance, and fate distribution in the groundwater of Hindukush ranges, Pakistan. The new estimates of USEPA equations record new research dimensions for carcinogenic and noncarcinogenic hazards. The principal component analysis (PCA), mineral phases, and spatial distribution determine groundwater contamination and its impacts. The average concentrations of PTEs, viz., Cd, Cu, Co, Fe, Pb, and Zn, were 0.06, 0.27, 0.07, 0.55, 0.05, and 0.19 mg/L, and E. coli, F. coli, and P. coli were 27.5, 24.0, and 19.0 CFU/100 ml. Moreover, the average values of basic minerals, viz., anhydrite, aragonite, calcite, dolomite, gypsum, halite, and hydroxyl apatite, were 0.4, 2.4, 2.6, 5.1, 0.6, and - 4.0, 11.2, and PTE minerals like monteponite, tenorite, cuprite, cuprous ferrite, cupric ferrite, ferrihydrite, goethite, hematite, lepidocrocite, maghemite, magnetite, massicot, minium, litharge, plattnerite, and zincite were - 5.5, 2.23, 4.65, 18.56, 20.0, 4.84, 7.54, 17.46, 6.66, 9.67, 22.72, - 3.36, 22.9, 3.16, - 18.0, and 1.46. The groundwater showed carcinogenic and non-carcinogenic health hazards for children and adults. The GWQI-model showed that 58.3% of samples revealed worse water quality. PCA revealed rock weathering, mineral dissolution, water-rock interaction, and industrial effluents as the dominant factors influencing groundwater chemistry. Carbonate weathering and ion exchange play vital roles in altering CaHCO3 type to NaHCO3 water. In this study, E. coli, F. coli, P. coli, EC, turbidity, TSS, PO43─, Na+, Mg+2, Ca+2, Cd, Co, Fe, and Pb have exceeded the World Health Organization (WHO) guidelines. The carcinogenic and non-carcinogenic impacts of PTEs and bacterial contamination declared that the groundwater is unfit for drinking and domestic purposes.

Pharmacognostic evaluation of Artemisia maritima L. a highly medicinal specie of genus Artemisia

The light and scanning electron microscopic observations were carried out for anatomical features of leaf, pollens and powder.Microscopic studies provide useful information for identification and authentication of adulteration in A. maritima. Nutritional analysis of A. maritima revealed that life fundamental macromolecules such as carbohydrates (49.63 %) crude proteins (13.17 %) and crude fibers (21.06 %) were present in sufficient quantity while crude fats (4.11 %) reported in low quantity. The life essential elements such as Mg (9.472 ± 0.011), Ca (4.152 ± 0.135) and Fe (4.112 ± 0.002) were found in high concentration while heavy metals reported under the safety threshold of WHO. These observations favored A. maritima an alternative of food.Appreciable quantity of phenolics (17.64 ± 0.574) and flavonoids (7.67 ± 0.069) were found while qualitatively active phytochemicals were reported.

The FTIR characterization of A. maritima crude powder revealed chromatogram in 3328.61 to 408.68 frequency range and 24 characteristic peaks on the basis of which different compounds of biological importance were classified. HPLC-UV technique quantifiedand identified six phenolic compounds morin,epigallocatechin gallate, catechin hydrate,ellagic acid, pyrogallol andrutin. Identification of compounds through GC–MS chromatogram revealed the presence of 46 compounds in methanolic fraction however 17 compounds of biological importance were selected.

In-vitro biological evaluation of A. maritima for antioxidant, antimicrobial, antidiabetic (12.61 ± 0.113 %) and cytotoxic activities (LC₅₀ = 20 μg/ml) suggested that methanolic fractions exhibited the highest activity as compared to chloroform and ethyl acetate fractions. The MIC values of 10 or 15 mg/ml were recorded for most of the fungal pathogens. Antibacterial activity revealed 3.75 mg/ml of MIC values against B. subtilis and 1.87 mg/ml against S. aureus, E. coli and P. aeruginosa. In-vivo biological evaluation revealed thatmaximum inhibition was observed for crude extract at 250 mg/kg body weight. The mechanism underlined in-vivo analgesic responses was carried out which revealed that naloxone (morphine and tramadol antagonist) showed no prominent effect while Glibenclamide pretreatment minutely modified the analgesic action. These observations clearly indicted the absence of opiod receptors and involvement of ATP sensitive potassium channels.

Biosynthesized silver nanoparticles using Polygonatum geminiflorum efficiently control fusarium wilt disease of tomato

Nanomaterials are gaining tremendous potential as emerging antimicrobials in the quest to find resistance-free alternatives of chemical pesticides. In this study, stable silver nanoparticles were synthesized using the aqueous extract of medicinal plant species Polygonatum geminiflorum, and their morphological features were evaluated by transmission electron microscopy, X-ray diffraction spectroscopy and energy dispersive X-ray analysis. In vitro Antifungal activity of the synthesized silver nanoparticles (AgNPs) and P. geminiflorum extract (PE) either alone or in combination (PE-AgNPs) against Fusarium oxysporum was evaluated using disc-diffusion and well-diffusion methods. In planta assay of the same treatments against Fusarium wilt diseases of tomato was evaluated by foliar spray method. Moreover, plant extract was evaluated for the quantitative investigation of antioxidant activity, phenolics and flavonoids by spectroscopic and HPLC techniques. Phytochemical analysis indicated the presence of total phenolic and flavonoid contents as 48.32 mg ± 1.54 mg GAE/g and 57.08 mg ± 1.36 mg QE/g, respectively. The DPPH radical scavenging of leaf extract was found to be 88.23% ± 0.87%. Besides, the HPLC phenolic profile showed the presence of 15 bioactive phenolic compounds. Characterization of nanoparticles revealed the size ranging from 8 nm to 34 nm with average crystallite size of 27 nm. The FTIR analysis revealed important functional groups that were responsible for the reduction and stabilization of AgNPs. In the in vitro assays, 100 μg/ml of AgNPs and AgNPs-PE strongly inhibited Fusarium oxysporum. The same treatments tested against Fusarium sprayed on tomato plants in controlled environment exhibited nearly 100% plant survival with no observable phytotoxicity. These finding provide a simple baseline to control Fusarium wilt using silver nano bio-control agents without affecting the crop health.

Iron and zinc micronutrients and soil inoculation of Trichoderma harzianum enhance wheat grain quality and yield

Malnutrition is mainly caused by iron and zinc micronutrient deficiencies affecting about half of the world's population across the globe. Biofortification of staple crops is the right approach to overcome malnutrition and enhance nutrient contents in the daily food of humans. This study aimed to evaluate the role of foliar application of iron and zinc in Trichoderma harzianum treated soil on various growth characteristics, quality, and yield of wheat varieties. Plants were examined in the absence/presence of T. harzianum, and iron and zinc micronutrients in both optimal and high-stress conditions. Although the symbiotic association of T. harzianum and common wheat is utilized as an effective approach for wheat improvement because of the dynamic growth promoting the ability of the fungus, this association was found tremendously effective in the presence of foliar feeding of micronutrients for the enhancement of various growth parameters and quality of wheat. The utilization of this approach positively increased various growth parameters including spike length, grain mass, biomass, harvest index, and photosynthetic pigments. The beneficial role of T. harzianum in combination with zinc and iron in stimulating plant growth and its positive impact on the intensities of high molecular weight glutenin subunits (HMW-GS) alleles make it an interesting approach for application in eco-friendly agricultural systems. Further, this study suggests a possible alternative way that does not merely enhances the wheat yield but also its quality through proper biofortification of iron and zinc to fulfill the daily needs of micronutrients in staple food.

Contamination level, source identification and health risk assessment of potentially toxic elements in drinking water sources of mining and non-mining areas of Khyber Pakhtunkhwa, Pakistan

Accelerated mining activities have increased water contamination with potentially toxic elements (PTEs) and their associated human health risk in developing countries. The current study investigated the distribution of PTEs, their potential sources and health risk assessment in both ground and surface water sources in mining and non-mining areas of Khyber Pakhtunkhwa, Pakistan. Water samples (n = 150) were taken from selected sites and were analyzed for six PTEs (Ni, Cr, Zn, Cu, Pb and Mn). Among PTEs, Cr showed a high mean concentration (497) μg L^-1, followed by Zn (414) μg L^-1 in the mining area, while Zn showed the lowest mean value (4.44) μg L^-1 in non-mining areas. Elevated concentrations of Ni, Cr and a moderate level of Pb in ground and surface water of Mohmand District exceeded the permissible limits set by WHO. Multivariate statistical analyses showed that the pollution sources of PTEs were mainly from mafic-ultramafic rocks, acid mine drainage, open dumping of mine wastes and mine tailings. The hazard quotient (HQ) was the highest for children relative to that for adults, but not higher than the USEPA limits. The hazard index (HI) for ingestions of all selected PTEs was lower than the threshold value (HI_ing < 1), except for Mohmand District, which showed a value of HI >1 in mining areas through ingestion. Moreover, the carcinogenic risk (CR) values exceeded the threshold limits for Ni and Cr set by the USEPA (1.0E-04-1.0E-06). In order to protect the drinking water sources of the study areas from further contamination, management techniques and policy for mining operations need to be implemented.

Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms

In this study, an integrated framework of artificial neural networks (ANNs) and metaheuristic algorithms have been developed for the prediction of biochar yield using biomass characteristics and pyrolysis process conditions. Comparative analysis of six different metaheuristic algorithms was performed to optimize the ANN architecture and select important features. The results suggested that the ANN model coupled with the Rao-2 algorithm outperformed (R² ∼ 0.93, RMSE ∼ 1.74%) all other models. Furthermore, the detailed information behind the models was acquired, identifying the most influencing factors as follows: pyrolysis temperature (56%), residence time (23%), and heating rate (8%). The partial dependence plot analysis revealed how each influencing factor affected the target variable. Finally, an easy-to-use software tool for predicting biochar yield was built using the ANN-Rao-2 model. This study demonstrates huge potential that machine learning presents in predictive modelling of complex pyrolysis processes, and reduces the time-consuming and expensive experimental work for estimating the biochar yield.

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

BACKGROUND

Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications.

METHODS

We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC).

FINDINGS

In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]).

INTERPRETATION

In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required.

FUNDING

British Journal of Surgery Society.