Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.

Synthesis, monomer conversion, and mechanical properties of polylysine based dental composites

OBJECTIVE

The aim of this study was to synthesize a new bioactive and antibacterial composite by incorporating reactive calcium phosphate and antibacterial polylysine into a resin matrix and evaluate the effect of these fillers on structural analysis, degree of monomer conversion, mechanical properties, and bioactivity of these newly developed polypropylene based dental composites.

METHODOLOGY

Stock monomers were prepared by mixing urethane dimethacrylate and polypropylene glycol dimethacrylate and combined with 40 wt% silica to make experimental control (E-C). The other three experimental groups contained a fixed percentage of silica (40 wt%), monocalcium phosphate monohydrate, and β-tri calcium phosphate (5 wt% each) with varying amounts of polylysine (PL). These groups include E-CCP0 (0 wt% PL), E-CCP5 (5 wt% PL) and E-CCP10 (10 wt% PL). The commercial control used was Filtek™ Z250 3M ESPE. The degree of conversion was assessed by using Fourier transform infrared spectroscopy (FTIR). Compressive strength and Vicker's micro hardness testing were evaluated after 24 h of curing the samples. For bioactivity, prepared samples were placed in simulated body fluid for 0, 1, 7, and 28 days and were analyzed using a scanning electron microscope (SEM). SPSS 23 was used to analyze the data and one-way ANOVA and post hoc tukey's test were done, where the significant level was set ≤0.05.

RESULTS

Group E-C showed better mechanical properties than other experimental and commercial control groups. Group E-C showed the highest degree of conversion (72.72 ± 1.69%) followed by E-CCP0 (72.43 ± 1.47%), Z250 (72.26 ± 1.75%), E-CCP10 (71.07 ± 0.19%), and lowest value was shown by E-CCP5 (68.85 ± 7.23%). In shear bond testing the maximum value was obtained by E-C. The order in decreasing value of bond strength is E-C (8.13 ± 3.5 MPa) > Z250 (2.15 ± 1.1 MPa) > E-CCP10 (2.08 ± 2.1 MPa) > E-CCP5 (0.94 ± 0.8 MPa) > E-CCP0 (0.66 ± 0.2 MPa). In compressive testing, the maximum strength was observed by commercial control i.e., Z250 (210.36 ± 18 MPa) and E-C (206.55 ± 23 MPa), followed by E-CCP0 (108.06 ± 19 MPa), E-CCP5 (94.16 ± 9 MPa), and E-CCP10 (80.80 ± 13 MPa). The maximum number of hardness was shown by E-C (93.04 ± 8.23) followed by E-CCP0 (38.93 ± 9.21) > E-CCP10 (35.21 ± 12.31) > E-CCP5 (34.34 ± 12.49) > Z250 (25 ± 2.61). SEM images showed that the maximum apatite layer as shown by E-CCP10 and the order followed as E-CCP10 > E-CCP5 > E-CCP0 >Z250> E-C.

CONCLUSION

The experimental formulation showed an optimal degree of conversion with compromised mechanical properties when the polylysine percentage was increased. Apatite layer formation and polylysine at the interface may result in remineralization and ultimately lead to the prevention of secondary caries formation.

Plasma GDNF levels in spinal dysraphism and its relation with neurological impairment in children: A point of care study

AIMS

GDNF plays a crucial role in the stimulation of recovery, neuroplasticity and synaptic reorganization after spinal cord injury providing neuroprotection and neuroregeneration. Plasma GDNF levels are upregulated in cases of spina bifida owing to the intrauterine damage of the exposed spinal cord. Our aim was to compare the plasma GDNF levels in patients of spina bifida with non-spina bifida cases and assess the correlation with neurological impairment at one year of follow up.

METHODS

Single centre prospective analysis of cases of spina bifida from 2020 to 2022 at presentation and after one year of follow up post-surgery. Cases with hernia and hydrocele without any other disorders were recruited into the control group. Plasma GDNF levels were assessed with immunoassay kits and compared with neurological involvement.

RESULTS

85 cases were included in the study. GDNF levels were elevated in cases compared to controls (mean 6.62 vs 1.76) with significant p value (<0.01). Same was observed for open and closed defects (mean 7.63 vs 4.86: p < 0.01). At follow up of 52 cases post-surgery cases with neurogenic bladder with abnormal urodynamic studies, sphincter involvement and motor impairment had significantly elevated baseline levels of GDNF compared with those who did not have this neurological impairment (p < 0.01).

DISCUSSION

The neurotrophic factor up-regulation can reflect an endogenous attempt at neuroprotection against the biochemical and molecular cascades triggered by the spinal cord damage. This upregulation can be represented as important biochemical markers of severe spinal cord damage and can be associated with severity of spine injury in MMC patients. Our results are in keeping with these findings, that, there were increased levels of plasma GDNF levels in cases of spinal dysraphism compared to control population. Also, the type of lesion reflecting the severity whether a closed or an open dysraphism, showed significant difference in levels between them suggesting, yet again, more damage in open defect as expected. The levels were higher with involvement of bladder, sphincter and lower limb power.

CONCLUSION

There is significant elevation of plasma GDNF levels in cases of spina bifida and this elevation is proportional to the degree of spinal damage and hence the neurological impairment. GDNF levels are a good predictor for assessing the severity of the lesion and thus the outcome in these cases. Additional prospective and long-term studies with a larger cohort are needed for a better understanding of neurotrophin pattern modulation in MMC.

Role of non-contrast CT component of prostate-specific membrane antigen PET/CT scan in the detection of peripheral zone prostate cancer

OBJECTIVE

The aim of this study was to look for feasibility of non-contrast CT (NCCT) in detecting peripheral zone prostate cancer (PCa).

METHODS

A retrospective analysis included 50 biopsy-proven PCa patients between April 2019 and March 2022 who underwent staging whole body prostate-specific membrane antigen (PSMA)/CT prior to treatment. The control subjects were 50 randomly selected adult male patients who underwent PET/CT for non-prostate malignancy during the same time period. Two readers independently calculated the Hounsfield unit (HU) of normal peripheral zone, central zone, and corresponding PSMA avid focus in cases.

RESULTS

No significant difference was seen in the mean HU value of normal peripheral zone between cases and controls. Significant difference in the mean HU was seen between the PSMA avid focus in cases (40.1 ± 6.2) and normal peripheral zone of cases (28.2 ± 7.0) and controls (27.7 ± 5.8). No significant difference was found between the mean HU values of high-grade PCa and non-high-grade PCa. Receiver operating characteristic (ROC) curve analysis revealed a mean HU cut-off of ≥35 for detecting PCa with a sensitivity and specificity of 86% and 90%, respectively, between cases and controls (AUC 0.88).

CONCLUSION

Detection of clinically significant PCa is possible on routinely performed NCCT scans. Radiologists should routinely look for and convey these findings to facilitate further work-up and early detection of PCa.

ADVANCES IN KNOWLEDGE

Our study adds to the knowledge that NCCT scans performed for unrelated indications can serve as a screening tool for clinically significant PCa.

Brachyolmia, dental anomalies and short stature (DASS): Phenotype and genotype analyses of Egyptian and Pakistani patients

Brachyolmia is a heterogeneous group of developmental disorders characterized by a short trunk, short stature, scoliosis, and generalized platyspondyly without significant deformities in the long bones. DASS (Dental Abnormalities and Short Stature), caused by alterations in the LTBP3 gene, was previously considered as a subtype of brachyolmia. The present study investigated three unrelated consanguineous families (A, B, C) with Brachyolmia and DASS from Egypt and Pakistan. In our Egyptian patients, we also observed hearing impairment. Exome sequencing was performed to determine the genetic causes of the diverse clinical conditions in the patients. Exome sequencing identified a novel homozygous splice acceptor site variant (LTBP3:c.3629-1G > T; p. ?) responsible for DASS phenotypes and a known homozygous missense variant (CABP2: c.590T > C; p.Ile197Thr) causing hearing impairment in the Egyptian patients. In addition, two previously reported homozygous frameshift variants (LTBP3:c.132delG; p.Pro45Argfs*25) and (LTBP3:c.2216delG; p.Gly739Alafs*7) were identified in Pakistani patients. This study emphasizes the vital role of LTBP3 in the axial skeleton and tooth morphogenesis and expands the mutational spectrum of LTBP3. We are reporting LTBP3 variants in seven patients of three families, majorly causing brachyolmia with dental and cardiac anomalies. Skeletal assessment documented short webbed neck, broad chest, evidences of mild long bones involvement, short distal phalanges, pes planus and osteopenic bone texture as additional associated findings expanding the clinical phenotype of DASS. The current study reveals that the hearing impairment phenotype in Egyptian patients of family A has a separate transmission mechanism independent of LTBP3.

Ammonia gas treatment in low cost biological reactor

Ammonia gas contributes to a number of environmental and human health concerns. The use of chalk, a cheap alkalinity source may reduce costs for biological systems. This research studies a closed liquid flow reactor to treat ammonia gas using chalk as biomass media and alkalinity source with high value calcium nitrate fertilizer production. The proposed reactor showed complete ammonia gas removal at high rate (500 mg N/L/day) and with low cost; where chalk dissolution and ammonia gas absorption contributed to alkalinity in the water for nitrification. High calcium ion concentration (up to 10,000 mg Ca2+ as CaCO3/L) showed only minor effects on ammonia absorption and nitrification rate.

Modifying dental composites to formulate novel methacrylate-based bone cements with improved polymerisation kinetics, and mechanical properties

OBJECTIVES

The aim was to develop bone composites with similar working times, faster polymerisation and higher final conversion in comparison to Cortoss™. Additionally, low shrinkage/heat generation and improved short and longer-term mechanical properties are desirable.

METHODS

Four urethane dimethacrylate based composites were prepared using tri-ethylene-glycol dimethacrylate (TEGDMA) or polypropylene dimethacrylate (PPGDMA) diluent and 0 or 20 wt% fibres in the glass filler particles. FTIR was used to determine reaction kinetics, final degrees of conversions, and polymerisation shrinkage/heat generation at 37 °C. Biaxial flexural strength, Young's modulus and compressive strength were evaluated after 1 or 30 days in water.

RESULTS

Experimental materials all had similar inhibition times to Cortoss™ (140 s) but subsequent maximum polymerisation rate was more than doubled. Average experimental composite final conversion (76%) was higher than that of Cortoss™ (58%) but with less heat generation and shrinkage. Replacement of TEGDMA by PPGDMA gave higher polymerisation rates and conversions while reducing shrinkage. Early and aged flexural strengths of Cortoss™ were 93 and 45 MPa respectively. Corresponding compressive strengths were 164 and 99 MPa. Early and lagged experimental composite flexural strengths were 164-186 and 240-274 MPa whilst compressive strengths were 240-274 MPa and 226-261 MPa. Young's modulus for Cortoss™ was 3.3 and 2.2 GPa at 1 day and 1 month. Experimental material values were 3.4-4.8 and 3.0-4.1 GPa, respectively. PPGDMA and fibres marginally reduced strength but caused greater reduction in modulus. Fibres also made the composites quasi-ductile instead of brittle.

SIGNIFICANCE

The improved setting and higher strengths of the experimental materials compared to Cortoss™, could reduce monomer leakage from the injection site and material fracture, respectively. Lowering modulus may reduce stress shielding whilst quasi-ductile properties may improve fracture tolerance. The modified dental composites could therefore be a promising approach for future bone cements.

Evaluation of Dual Dye Technique for Sentinel Lymph Node Biopsy in Breast Cancer: Two-Arm Open-Label Parallel Design Non-Inferiority Randomized Controlled Trial

INTRODUCTION

Radioisotope and blue dye are standard agents for performing sentinel lymph node (SLN) biopsy in breast cancer. The paucity of nuclear medicine facility poses logistic challenge. This study evaluated performance of radioisotope & methylene blue (MB) with indocyanine green (ICG) and MB for SLNB.

PATIENTS AND METHODS

This randomized controlled trial was conducted from December 2019 to July 2022 comparing SLN identification proportions of radioisotope-blue dye [Group A] with dual dye (MB + ICG; Group B]. Secondary objective included time required and cost effectiveness of performing SLNB. Sample size of 70 (35 in each arm) was calculated. Upfront operable node negative early breast cancer was included in the study. Clinico-demographic data, number & type of SLN, time taken were noted. Cost analysis was done including the equipment, manpower & consumables. Chi-square/Fisher exact test was used to compare proportion between two groups. p value of less than 0.05 was considered to represent statistical significance.

RESULTS

Seventy patients randomized to either group were similar in clinico-demographic and tumor characteristics. SLN identification rate (IR) was 91.43% in group A and 100% in group B. Overall IR of MB, radioisotope and ICG were 91.43%, 91.43% and 100%, respectively. Mean number of SLNs identified were 3 in group A and 4 in group B. Median time required for SLNB was 12 min and 14 min in either group, respectively. Cost of performing SLNB was higher in Group B.

CONCLUSION

SLNB using dual dye is non-inferior to radioisotope-blue dye in upfront operable early breast cancer. Trial registration number Clinical Trial registry India CTRI/2020/02/023503.

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

Urinary tract infection caused by Lactococcus garvieae in a premature neonate: A case report

Lactococcus garvieae is a gram-positive cocci that has primarily been described as a pathogen in various fish species, but has increasingly been reported to cause endocarditis and other infections in humans [1]. Neonatal infection caused by Lactococcus garvieae has not been previously reported. Here we describe a premature neonate who developed a urinary tract infection with this organism and was successfully treated with vancomycin therapy.

"Smartphone as an educational tool" the perception of dental faculty members of all the dental colleges of Khyber Pakhtunkhwa - Pakistan

BACKGROUND

With the increasing advancement in the field of information technology, it's about time we realize that our future will be shaped by this field. With more and more people using smartphones, we need to adapt them to the medical field. Already many advancements in medical field are done thanks to the advancement of computer science. But we need to implement this into our teaching and learning as well. Almost all students and faculty members use smartphones in one way or another if we can utilize the smartphone to enhance the learning opportunities for our medical students, it would greatly benefit them. But before the implementation, we need to find out if our faculty is willing to adopt this technology. The objective of this study is to find out what are the perceptions of dental faculty members about using a smartphone as a teaching tool.

METHODOLOGY

A validated questionnaire was distributed among the faculty members of all the dental colleges of KPK. The questionnaire had 2 sections. First one contains information regarding the demographics. The second one had questions related to the faculty members' perception regarding using a smartphone as a teaching tool.

RESULTS

The results of our study showed that the faculty (Mean 2.08) had positive perceptions regarding using a smartphone as a teaching tool.

CONCLUSION

Most of the Dental Faculty members of KPK agree that smartphone can be used as a teaching tool, and it can have better outcomes if proper applications and teaching strategies are used.

Microplastics assessment in Arabian Sea fishes: accumulation, characterization, and method development

Around the globe, plastic has been entering the aquatic system and is ingested by organisms. Identification, optimal digestion method, and characterization of the polymers to trace sources are of growing importance. Hence, the present work investigated microplastics accumulation, digestion protocol efficiency, and characterization of polymers with FTIR analysis in the guts of five fishes (Lethrinus nebulosus, Rastrelliger kanagurta, Acanthopagrus arabicus, Otolithes ruber, and Euryglossa orientalis) from the Karachi coastal area, Arabian Sea. A total of 1154 microplastics (MPs) were ingested by 29 out of 33 fish species (87%). The highest average MP/fish was recorded in Otolithes ruber (54) and the lowest in Rastrelliger kanagurta (19.42). Microfibers were the most abundant shape with the highest numbers (35.52%) as compared to the rest of the MPs identified. Transparent microfibers were recorded as the highest in numbers followed by red, black, blue, and green. In this study, KOH with different concentrations and exposure times along with oxidizing agent hydrogen peroxide was tested (Protocols 3 and 4). Results showed these bases were highly efficient in obtaining optimal digestion of the samples. FTIR analysis confirmed that the majority of the polymers found in the fish guts were polyethylene and polypropylene. This study validated for the first time the presence of these polymers of plastic in marine fish from Pakistan.

A commercial monovalent canine parvovirus vaccine performs better than a commercial combination vaccine in puppies

Thirty puppies were randomly divided in to 3 groups for comparative evaluation of two commercial CPV vaccines. Each group was further subdivided in to < 6 months and < 3 months -old puppies and either vaccinated with a monovalent vaccine: Primodog, a combination vaccine: Duramune or maintained as a non-vaccinated control. Humoral immune response was determined by Hemagglutination Inhibition (HAI) on 21 and 35 -days after vaccination. The geometric mean titer (GMT) induced by Duramune, 21 and 35 -days post-vaccination was GMT 73.3 and 137.2, respectively. Comparatively, Primodog demonstrated higher GMT on 21 and 35 -days after vaccination: 97.0 and 168.9, respectively. The older puppies (< 6 months old) demonstrated higher seroconversion to both vaccines.

COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%.

HLA B27 in Ankylosing Spondylitis: Differences in Frequency and Relative Risk in American Blacks and Caucasians

Twenty-eight HLA alleles of the A and B loci were determined in 23 American Blacks and 50 Caucasians with primary ankylosing spondylitis (AS). The prevalence of HLA B27 was significantly increased in American Black patients (48 per cent) vs Black controls (two per cent), but was much less than the 94 per cent found in Caucasian patients (controls eight per cent). The lower prevalence of B27 in American Black patients vs Caucasian patients was significant (p < 0.001), and indicated that susceptibility to AS is not as closely associated with B27 in Blacks as in Caucasians. No other HLA antigen was significantly associated with AS in either racial group. Among B27 positive individuals, the relative risk of developing AS was significantly lower in American Blacks than in Caucasians. These data indicate that for diagnostic purposes, the absence of B27 is less important in ruling out AS in Blacks than in Caucasians.

[Medical rehabilitation of children with obstructive uropathy]

Obstructive uropathy in children is predominantly urinary system malformation and one of the leading causes of chronic renal failure. Antenatal ultrasound can detect obstructive uropathy in infants. It is important to conduct diagnostics not only to identify the obstruction level in urinary system, but to assess renal function, renal blood flow and urination. Children are given conservative and surgical treatment methods to restore urodynamics, prevent infectious complications, improve renal blood flow. Currently, there are no principles, approaches and technologies for medical rehabilitation of patients with obstructive uropathy, therefore, the use of selective chromotherapy, which has an activating effect on regional circulation, and sound stimulation improving muscles tone of pelvis and ureters, is pathogenetically reasonable and promising.

OBJECTIVE

To develop technologies of physiotherapy application (sound stimulation, selective chromotherapy) for inclusion in comprehensive medical rehabilitation of children with megaloureter.

MATERIAL AND METHODS

Clinical observations and special examinations have been performed in 90 children with megaloureter aged from 1 to 10 years. The patients were divided into 2 groups: 30 children (study group) received sound stimulation combined with selective chromotherapy; 30 children (the 1st comparison group) - sound stimulation; 30 children (the 2nd comparison group) - selective chromotherapy (blue spectrum). General clinical methods, ultrasound of kidneys and bladder with Doppler monitoring of intrarenal blood flow, functional methods of bladder examination were used.

RESULTS

The positive effects of separate and combined application of sound stimulation and selective chromotherapy on clinical and laboratory indicators, urodynamics of urinary tract and renal blood flow in children with megaloureter after surgery have been revealed. The efficacy of selective chromotherapy use in children with megaloureter and comorbid neurologic bladder dysfunction has been proven.

CONCLUSION

Modern technologies for the application of physiotherapy, namely selective chromotherapy and sound stimulation, to include them in the comprehensive medical rehabilitation of children with megaloureter, have been developed for the first time and their high efficacy has been proven.

[The high-intensity pulsed magnetic therapy in the medical rehabilitation of children. (Literature review)]

In recent decades, a promising area of physiotherapy has been intensively developed In Russia and abroad - magnetic therapy, based on the use of various types of magnetic fields for preventive, curative and rehabilitative purposes. The use of high-intensity pulsed magnetotherapy is promising. The effectiveness of the method in a number of diseases of childhood, which has an active stimulating effect on the state of the neuromuscular apparatus, has been proven.

Successful Pregnancy with SLE-associated Antiphospholipid Syndrome: A Case Report

Pregnancy in women with systemic lupus erythematosus (SLE) is associated with an increased risk of adverse maternal and fetal outcomes. Risk is significantly increased when SLE pregnancy is complicated by anti-phospholipid syndrome (APS). Here, we present a case of a 21 year-old multi-gravid lady with SLE- associated APS who was diagnosed as such when she presented with multisystem flare at her 16 weeks of gestation. At presentation she had fever, multiple joint pain in both upper and lower limbs, loss of hair, history of recurrent oral ulcer, skin rash over hand and feet. Physical examination and laboratory evaluation were consistent with an active SLE flare. A diagnosis of antiphospholipid syndrome (APS) was made based on her clinical presentation and laboratory findings. The reported patient had APS secondary to SLE. She had all the risk factors that would confer a remarkably high risk of pregnancy morbidity: positive anti-SSA(RO) antibody and lupus anticoagulant, history of one neonatal death due to congenital heart block and two consecutive first trimester pregnancy loss. Multidisciplinary management approach with appropriate intervention and close monitoring can bring a successful outcome.

Evaluation of HE4 as a prognostic biomarker in uterine cervical cancer. Cancer Treat Res Commun 2023;34:100672. [PMID: 36525756 DOI: 10.1016/j.ctarc.2022.100672]

INTRODUCTION

Uterine cervical cancer (UCC) is the fourth most common health problem worldwide among women. Currently available biomarkers CA125, CA199, and CEA for diagnosis or prognostic evaluation of UCC have not got widespread acceptance.

METHOD

Whole blood samples of 64 patients with UCC were collected along with 63 healthy females and tested for serum levels of HE4 (sHE4). A cut-off value for positive result 64.0 pmol/L was set. Statistical analysis of different clinical variables was done.

RESULT

Serum level of HE4 has a significant role in the diagnosis of uterine cervical cancer. Its level increases with age, higher parity (P < 0.05), stage (P < 0.16), tumor size, and parametrial invasion. Negative result was seen with vaginal invasion, lymph node involvement & cases which had recurrence. Various histological types showed variable results. So the serum level of HE4 (sHE) level may play a role in the diagnosis & therapeutic monitoring of UCC. But the prognostic evaluation needs further studies.

CONCLUSION

sHE4 is useful in the diagnosis of cervical cancer, but its prognostic significance is under the question marks. It may be associated with higher values in higher stages. Higher parity of the patient is associated with higher level of HE4 in UCC.

Molecular epidemiology and phylogenetic analysis of Tams I gene of Theileria annulata in Khyber Pakhtunkhwa, Pakistan

Theileriosis is a hemoparasitic disease that affects a wide range of different animal species and is caused by various species of Theileria. This study aimed to determine the molecular epidemiology of Theileria annulata through microscopy and PCR, in crossbred cattle in some districts of Khyber Pakhtunkhwa, Pakistan. For this study, a total of 384 blood samples were collected from cattle in the Peshawar (n=120), Charsadda (n=94), Nowshera (n=84), and Swabi (n=86) districts. Microscopy and PCR were used to determine the overall prevalence of theileriosis, which was found to be 15.8 and 22.6%, respectively. Theileria annulata was detected in blood samples through PCR in the study area, and the target gene i.e., Tams 1, of positive samples was sequenced. The sequences in the current study revealed high sequence homology (ranging from 96 to 100%) with Tams 1 sequences of neighboring countries present in the NCBI database. Season, breed, age, and sex were found to be important risk factors among the several risk factors examined, whereas, among different clinical manifestations, lymphadenopathy showed a strong association with theileriosis. According to Cohen's kappa and ROC analysis, microscopy was proven to be a fair diagnostic test for detecting theileriosis in cattle, and may be used in combination with molecular techniques for screening a large number of animals.

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.

295 SPEP IT UP! DEVELOPING AN ALGORITHM FOR ABNORMAL SERUM PLASMA ELECTROPHORESIS RESULTS IN HIP FRACTURE PATIENTS

Background

Hip fracture is a common manifestation of osteoporosis. All patients who sustain a hip fracture should receive a specialist bone health assessment, including Serum Protein Electrophoresis (SPEP) because plasma cell disorders such as multiple myeloma are an important differential diagnosis. SPEP results can be challenging to interpret without training and expertise. We aimed to review the proportion of abnormal SPEP results in hip fracture patients and used a newly developed algorithm to assess urgency of referral to haematology.

Methods

The Orthogeriatrics and Haematology teams collaborated to develop an algorithm to help facilitate decision making in hip fracture patients with abnormal SPEP results. A retrospective study was then conducted using data from the local Hip Fracture Database from Quarters 1 and 3 in 2020, and the hospital electronic laboratory system. The algorithm was used to retrospectively determine which patients warranted haematology review. The electronic appointment system was then accessed to review whether those who warranted haematology referral had appointments on the system.

Results

Of 270 hip fracture presentations, 19 duplicate records were excluded. Five patients had no data and three patients had passed away. Of the remaining 243 patients, 193 (79.42%) had SPEP’s sent. Abnormalities were detected in 116 patients (47.74%). According to the SPEP referral pathway, two patients warranted routine referral and one patient required an urgent referral, none of whom appeared to have been referred to haematology. Two patients who did not warrant haematology referral were already under haematology for different conditions.

Conclusion

Not all patients who sustain acute osteoporotic fractures with an abnormal SPEP result require haematology referral. The need for an urgent or routine haematology can be guided by the SPEP result along with other clinical features. With the introduction of this pathway, it is proposed that all hip fracture patients will be triaged in a timely, appropriate, and consistent manner.

Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning

COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Community- vs. hospital-based management of multidrug-resistant TB in Pakistan

BACKGROUND Multidrug-resistant TB (MDR-TB) treatment takes 18-24 months and is complex, costly and isolating. We provide trial evidence on the WHO Pakistan recommendation for community-based care rather than hospital-based care.METHODS Two-arm, parallel-group, superiority trial was conducted in three programmatic management of drug-resistant TB hospitals in Punjab and Sindh Provinces, Pakistan. We enrolled 425 patients with MDR-TB aged >15 years through block randomisation in community-based care (1-week hospitalisation) or hospital-based care (2 months hospitalisation). Primary outcome was treatment success.RESULTS Among 425 patients with MDR-TB, 217 were allocated to community-based care and 208 to hospital-based care. Baseline characteristics were similar between the community and hospitalised arms, as well as in selected sites. Treatment success was 74.2% (161/217) under community-based care and 67.8% (141/208) under hospital-based care, giving a covariate-adjusted risk difference (community vs. hospital model) of 0.06 (95% CI -0.02 to 0.15; P = 0.144).CONCLUSIONS We found no clear evidence that community-based care was more or less effective than hospital-based care model. Given the other substantial advantages of community-based care over hospital based (e.g., more patient-friendly and accessible, with lower treatment costs), this supports the adoption of the community-based care model, as recommended by the WHO.

Towards Parallel Selective Attention Using Psychophysiological States as the Basis for Functional Cognition

Attention is a complex cognitive process with innate resource management and information selection capabilities for maintaining a certain level of functional awareness in socio-cognitive service agents. The human-machine society depends on creating illusionary believable behaviors. These behaviors include processing sensory information based on contextual adaptation and focusing on specific aspects. The cognitive processes based on selective attention help the agent to efficiently utilize its computational resources by scheduling its intellectual tasks, which are not limited to decision-making, goal planning, action selection, and execution of actions. This study reports ongoing work on developing a cognitive architectural framework, a Nature-inspired Humanoid Cognitive Computing Platform for Self-aware and Conscious Agents (NiHA). The NiHA comprises cognitive theories, frameworks, and applications within machine consciousness (MC) and artificial general intelligence (AGI). The paper is focused on top-down and bottom-up attention mechanisms for service agents as a step towards machine consciousness. This study evaluates the behavioral impact of psychophysical states on attention. The proposed agent attains almost 90% accuracy in attention generation. In social interaction, contextual-based working is important, and the agent attains 89% accuracy in its attention by adding and checking the effect of psychophysical states on parallel selective attention. The addition of the emotions to attention process produced more contextual-based responses.

Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR.

Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology

The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes.

A Novel Grayscale Image Encryption Scheme Based on the Block-Level Swapping of Pixels and the Chaotic System

Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, not suitable for many critical applications (e.g., telesurgery). Security is of the utmost importance while writing these schemes. This study aimed to provide a scheme based on block-level scrambling (with increased speed). Three streams of chaotic data were obtained through the intertwining logistic map (ILM). For a given image, the algorithm creates blocks of eight pixels. Two blocks (randomly selected from the long array of blocks) are swapped an arbitrary number of times. Two streams of random numbers facilitate this process. The scrambled image is further XORed with the key image generated through the third stream of random numbers to obtain the final cipher image. Plaintext sensitivity is incorporated through SHA-256 hash codes for the given image. The suggested cipher is subjected to a comprehensive set of security parameters, such as the key space, histogram, correlation coefficient, information entropy, differential attack, peak signal to noise ratio (PSNR), noise, and data loss attack, time complexity, and encryption throughput. In particular, the computational time of 0.1842 s and the throughput of 3.3488 Mbps of this scheme outperforms many published works, which bears immense promise for its real-world application.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

Breast Cancer Detection and Classification Empowered With Transfer Learning

Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

Corona Virus Disease 2019 (COVID-19) Diagnostic Tests: A Glimpse

Mass testing for COVID-19 infection is one of the core measures in tackling the global spread of the disease. Testing is vital to diagnose and estimate cases, attack rates and case fatality rates- critical data for policy-making. As COVID-19 continues to spread globally, the demand for more extensive laboratory testing and innovative technology increases. However, countries around the world have been struggling to keep up pace with the worldwide demand to expand testing strategy. The pandemic evolves, so does our knowledge and understanding of diagnostic tests of COVID-19. Here we aim to review major challenges related to COVID-19 diagnostic tests and future development. So, the ongoing urgency and demand for tests would certainly steer the rapid uptake of novel techniques, which in turn would boost our understanding of diagnostic tests for COVID-19.

Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning

Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities.

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

Identification of SCAR markers for genetic authentication of Dendrobium nobile Lindl

Dendrobium nobile Lindl. is an orcid plant with important medicinal values. This is a colourful houseplant, and also a popular herb in traditional Chinese medicine (TCM). The variants of this plant from different geographic regions might be high, and in this study, we aimed to develop specific sequence characterized amplified region (SCAR) markers for the identification of specific variant of this plant. Different cultivars of D. nobile were collected from nine different places of China, and one cultivar from Myanmar. DNA materials were extracted from the plant samples, random amplified polymorphic DNA (RAPD) were developed, cloned and sequenced for the development of SCAR markers. We have developed four SCAR markers, which are specific to the cultivar from Luzhou China, and clearly distinguishable (genetically) from other cultivars. These SCAR markers are deposited in GenBank (accession number MZ417502, MZ484089, MZ417504 and MZ417505). Four SCAR markers for D. nobile are effective molecular technique to genetically identify the different cultivars or species, and this method is applicable for genetic characterization and identification of other plant species too.

Impact of COVID-19 pandemic on quality of partner relationship and sexual activity among COVID positive males: a cross sectional study

OBJECTIVE

Our aim was to assess sexual activity, partner relationships among males who had been infected with COVID-19, to study the impact of COVID-19 infection on partner relationship and to find out the association between partner and sexual relationship during lockdown.

MATERIALS AND METHODS

A cross sectional study was conducted in Saudi Arabia through social media platforms via online questionnaire between December 1, 2020 and January 31, 2021 among 871 participants after a pilot study among 20 participants of which 497 were included in the study. Statistical analysis was conducted using SPSS version 20.0 (IBM Inc., Armonk, NY, USA). Responses were presented as frequencies and percentages and the association was studied using Chi squared test/Fisher's exact test. The value of p ≤ .05 was considered significant.

RESULTS

Out of the total study participants, nearly 85% of them belonged to the age range of 18 to 39 years, more than half of the participants were married. In the six months prior to the study being conducted, 268 respondents (53.9%) did not have sexual relationships. Respondents with positive COVID-19 infection reported that their partner lived with them in the same house during home isolation and was also found to be significantly associated with having intact sexual relationships in the last six months of the lockdown period (p-value < .001). Moreover, respondents who reported having good relationships with their partners during the pandemic were found to be significantly associated with having intact sexual relationships during the pandemic lockdown (p-value < .001).

CONCLUSIONS

Among the COVID-19-positive respondents, sexual activity and partner relationships were largely found to be intact during the pandemic lockdown period.

Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization

Cancer is a manifestation of disorders caused by the changes in the body's cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.

Activity of Fosfomycin Against The Spectrum of Uropathogens Causing Cystitis

Background:

Urinary tract infections (UTIs) are the most frequent bacterial infections, commonly seen in females. High degree of antimicrobial resistance in uropathogens has challenged the use of therapeutic agents. Fosfomycin which is an old antibiotic with distinctive characteristics, seems to be a promising novel therapeutic agent with a good bactericidal activity towards multi-drug resistant (MDR) uropathogens.

Objective:

The main objective of the study is to evaluate the antibacterial activity of fosfomycin among uropathogens causing cystitis.

Methods:

The study was carried out between 2017-2018. A total of 2060 UTI suspects from outpatient department (OPDs) and inpatient department (IPDs) were screened. Out of 2060 screened patients 1658 were IPD patients and 402 were OPD patients. Patient’s midstream urine samples were collected aseptically and processed according to standard protocols. The frequency of extended-spectrum-beta lactamases (ESBLs) producer and carbapenem resistance were estimated respectively. Cultures with significant growth of uropathogens were identified and minimum inhibitory concentration (MIC) to fosfomycin was determined by agar dilution (AD) and by E-test methods.

Results:

184 out of 2060 (8.9%) urine samples showed significant growth of uropathogens. Uropathogenic Escherichia coli (UPEC) (64%,118/184) was found to be the mostly isolated uropathogen. Among these Gram-negative uropathogens, 80% were ESBLs producers, 43.2% were carbapenem-resistant and 78% isolates were found to be MDR. The fosfomycin susceptibility for UPEC was 95% by AD method.

Conclusions:

This study suggests that Fosfomycin is reasonably effective and can be used in the treatment of MDR uropathogens along with uncomplicated UTIs.

Juglans Regia (Walnut Tree) Bark in Dentistry

Juglans regia, commonly known as the Walnut tree, is a type of a deciduous tree. The tree has many important parts, the seed, bark, husk, leaves, oil, shell of the fruit and the kernel. The plant has been used in its crude form since ages. The kernel holds nutritional value. The leaves contain an essential oil which is extracted and used. The husk contains steroids and vitamins amongst other useful compounds. The leaves are used topically as antipyretic, analgesic, antidandruff and to heal burns. The bark is tough and has been used for mechanical tooth cleaning due to its tough fibrous texture. It contains Juglone as its main and most important constituent. Juglone works as an anti-viral, anti-parasitic, anti-fungal, anti-bacterial, anti-inflammatory, and anti-cancerous agent. In dentistry it poses as an effective anti-plaque, anti-fungal, anti-bacterial, anti-cariogenic and tooth whitening material. It was concluded that in recent years, scientists and researchers have shown increasing interest towards the in depth understanding of the chemicals and compounds of the bark and its utilization in dental products towards improving dental treatment.    The author would like to thank ORIC, Khyber Medical University for assistance in publication Reference No: KMU/ORIC/AR/005.

Juglans Regia (Walnut Tree) Bark in Dentistry

Juglans regia, commonly known as the Walnut tree, is a type of a deciduous tree. The tree has many important parts, the seed, bark, husk, leaves, oil, shell of the fruit and the kernel. The plant has been used in its crude form since ages. The kernel holds nutritional value. The leaves contain an essential oil which is extracted and used. The husk contains steroids and vitamins amongst other useful compounds. The leaves are used topically as antipyretic, analgesic, antidandruff and to heal burns. The bark is tough and has been used for mechanical tooth cleaning due to its tough fibrous texture. It contains Juglone as its main and most important constituent. Juglone works as an anti-viral, anti-parasitic, anti-fungal, anti-bacterial, anti-inflammatory, and anti-cancerous agent. In dentistry it poses as an effective anti-plaque, anti-fungal, anti-bacterial, anti-cariogenic and tooth whitening material. It was concluded that in recent years, scientists and researchers have shown increasing interest towards the in depth understanding of the chemicals and compounds of the bark and its utilization in dental products towards improving dental treatment.    The author would like to thank ORIC, Khyber Medical University for assistance in publication Reference No: KMU/ORIC/AR/005.

The First Report of a Missense Variant in RFX2 Causing Non-Syndromic Tooth Agenesis in a Consanguineous Pakistani Family

Background: The syndromic and non-syndromic congenital missing teeth phenotype is termed tooth agenesis. Since tooth agenesis is a heterogeneous disorder hence, the patients show diverse absent teeth phenotypes. Thus identifying novel genes involved in the morphogenesis of ectodermal appendages, including teeth, paves the way for establishing signaling pathways.

Methods and Results: We have recruited an autosomal recessive non-syndromic tooth agenesis family with two affected members. The exome sequencing technology identified a novel missense sequence variant c.1421T &gt; C; p.(Ile474Thr) in a regulatory factor X (RFX) family member (RFX2, OMIM: 142,765). During the data analysis eight rare variants on various chromosomal locations were identified, but the co-segregation analysis using Sanger sequencing confirmed the segregation of only two variants RFX2: c.1421T &gt; C; p.(Ile474Thr), DOHH: c.109C &gt; G; p.(Pro37Ala) lying in a common 7.1 MB region of homozygosity on chromosome 19p13.3. Furthermore, the online protein prediction algorithms and protein modeling analysis verified the RFX2 variant as a damaging genetic alteration and ACMG pathogenicity criteria classified it as likely pathogenic. On the other hand, the DOHH variant showed benign outcomes.

Conclusion:RFX2 regulates the Hedgehog and fibroblast growth factor signaling pathways, which are involved in the epithelial and mesenchymal interactions during tooth development. Prior animal model studies have confirmed the expression of rfx2 at a developmental stage governing mouth formation. Moreover, its regulatory role and close association with ciliary and non-ciliary genes causing various dental malformations makes it a potential candidate gene for tooth agenesis phenotype. Further studies will contribute to exploring the direct role of RFX2 in human tooth development.

[Medical rehabilitation for children with scoliosis]

The article presents a literature review on the prevalence, relevance, social significance, and principles of medical rehabilitation of children with different types of scoliosis in scoliotic disease. The current classification, diagnostics features, and clinical course of the disease are addressed. Current approaches to the choice of medical rehabilitation methods for scoliotic disease in children are described: therapeutic exercise, hydrokinesiotherapy, massage, physiotherapeutic treatment, kinesiotaping, and corseting. Special consideration is given to postoperative management and stages of medical rehabilitation of children with scoliosis, including resort treatment.

Anti-hyperglycemic and anti-hyperlipidemic effects of a methanolic extract of Debregeasia salicifolia in Alloxan-induced diabetic albino mice

Diabetes mellitus (DM), an endocrine syndrome characterized by high blood glucose levels due to abrogated insulin activity. The existing treatments for DM have side effects and varying degrees of efficacy. Therefore, it is paramount that novel approaches be developed to enhance the management of DM. Therapeutic plants have been accredited as having comparatively high efficacy with fewer adverse effects. The current study aims to elucidate the phytochemical profile, anti-hyperlipidemic, and anti-diabetic effects of methanolic extract D. salicifolia (leaves) in Alloxan-induced diabetic mice. Alloxan was injected intraperitoneally (150 mg kg-1, b.w), to induced diabetes in mice. The mice were divided into three groups (n=10). Group 1 (normal control) received normal food and purified water, Group II (diabetic control) received regular feed and clean water and group III (diabetic treated) received a methanolic extract of the plant (300 mg kg-1) for 28 days with a typical diet and clean water throughout the experiment. Blood samples were collected to checked serum glucose and concentration of LDL, TC, TG. The extract demonstrated significant antihyperglycemic activity (P<0.05), whereas improvements in mice's body weight and lipid profiles were observed after treatment with the extract. This study establishes that the extract has high efficacy with comparatively less toxicity that can be used for DM management.

A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.