The Association between Lifestyle Factors and COVID-19: Findings from Qatar Biobank

Coronavirus Disease 2019 (COVID-19) manifestations range from mild to severe life-threatening symptoms, including death. COVID-19 susceptibility has been associated with various factors, but studies in Qatar are limited. The objective of this study was to investigate the correlation between COVID-19 susceptibility and various sociodemographic and lifestyle factors, including age, gender, body mass index, smoking status, education level, dietary patterns, supplement usage, physical activity, a history of bariatric surgery, diabetes, and hypertension. We utilized logistic regression to analyze these associations, using the data of 10,000 adult participants, aged from 18 to 79, from Qatar Biobank. In total, 10.5% (n = 1045) of the participants had COVID-19. Compared to non-smokers, current and ex-smokers had lower odds of having COVID-19 (odds ratio [OR] = 0.55; 95% CI: 0.44-0.68 and OR = 0.70; 95% CI: 0.57-0.86, respectively). Vitamin D supplement use was associated with an 18% reduction in the likelihood of contracting COVID-19 (OR = 0.82; 95% CI: 0.69-0.97). Obesity (BMI ≥ 30 kg/m2), a history of bariatric surgery, and higher adherence to the modern dietary pattern-characterized by the consumption of foods high in saturated fat and refined carbohydrates-were positively associated with COVID-19. Our findings indicate that adopting a healthy lifestyle may be helpful in the prevention of COVID-19 infection.

The Parkinson's disease risk gene cathepsin B promotes fibrillar alpha-synuclein clearance, lysosomal function and glucocerebrosidase activity in dopaminergic neurons

Background

Variants in the CTSB gene encoding the lysosomal hydrolase cathepsin B (catB) are associated with increased risk of Parkinson's disease (PD). However, neither the specific CTSB variants driving these associations nor the functional pathways that link catB to PD pathogenesis have been characterized. CatB activity contributes to lysosomal protein degradation and regulates signaling processes involved in autophagy and lysosome biogenesis. Previous in vitro studies have found that catB can cleave monomeric and fibrillar alpha-synuclein, a key protein involved in the pathogenesis of PD that accumulates in the brains of PD patients. However, truncated synuclein isoforms generated by catB cleavage have an increased propensity to aggregate. Thus, catB activity could potentially contribute to lysosomal degradation and clearance of pathogenic alpha synuclein from the cell, but also has the potential of enhancing synuclein pathology by generating aggregation-prone truncations. Therefore, the mechanisms linking catB to PD pathophysiology remain to be clarified.

Methods

Here, we conducted genetic analyses of the association between common and rare CTSB variants and risk of PD. We then used genetic and pharmacological approaches to manipulate catB expression and function in cell lines and induced pluripotent stem cell-derived dopaminergic neurons and assessed lysosomal activity and the handling of aggregated synuclein fibrils.

Results

We first identified specific non-coding variants in CTSB that drive the association with PD and are linked to changes in brain CTSB expression levels. Using iPSC-derived dopaminergic neurons we then find that catB inhibition impairs autophagy, reduces glucocerebrosidase (encoded by GBA1) activity, and leads to an accumulation of lysosomal content. Moreover, in cell lines, reduction of CTSB gene expression impairs the degradation of pre-formed alpha-synuclein fibrils, whereas CTSB gene activation enhances fibril clearance. Similarly, in midbrain organoids and dopaminergic neurons treated with alpha-synuclein fibrils, catB inhibition or knockout potentiates the formation of inclusions which stain positively for phosphorylated alpha-synuclein.

Conclusions

The results of our genetic and functional studies indicate that the reduction of catB function negatively impacts lysosomal pathways associated with PD pathogenesis, while conversely catB activation could promote the clearance of pathogenic alpha-synuclein.

Development and Evaluation of a High-Fidelity Rhinoplasty Simulator

SUMMARY

Rhinoplasty is a challenging procedure with a steep learning curve. Surgical simulators provide a safe platform to gain hands-on experience without compromising patient outcomes. Therefore, rhinoplasty is an ideal procedure to benefit from an effective surgical simulator. A high-fidelity rhinoplasty simulator was developed using three-dimensional computer modeling, three-dimensional printing, and polymer techniques. The simulator was tested by six surgeons with experience in rhinoplasty to assess realism, anatomic accuracy, and value as a training tool. The surgeons performed common rhinoplasty techniques and were provided a Likert-type questionnaire assessing the anatomic features of the simulator. A variety of surgical techniques were performed successfully using the simulator, including open and closed approaches. Bony techniques performed included endonasal osteotomies and rasping. Submucous resection with harvest of septal cartilage, cephalic trim, and tip suturing, as well as grafting techniques including alar rim, columellar strut, spreader, and shield grafts, were performed successfully. Overall, there was agreement on the simulator's anatomic accuracy of bony and soft-tissue features. There was strong agreement on the simulator's overall realism and value as a training tool. The simulator provides a high-fidelity, comprehensive training platform to learn rhinoplasty techniques to augment real operating experience without compromising patient outcomes.

Qatar's Silent Epidemic: A Comprehensive Meta-analysis on the Prevalence of Metabolic Syndrome

BACKGROUND

Metabolic syndrome comprises various conditions like abdominal obesity, insulin resistance, elevated triglyceride levels, reduced HDL, and high blood pressure, which pose significant health challenges globally. It's imperative to determine its prevalence in specific populations to formulate effective preventive measures.

OBJECTIVE

This systematic review and meta-analysis aimed to determine the prevalence of metabolic syndrome in the Qatari population.

METHODS

Using the PRISMA guidelines, a systematic search was executed on PubMed until July 2023 with keywords "Metabolic syndrome" and "Qatar." Eligibility criteria included human subjects, studies assessing metabolic syndrome components, and research conducted in Qatar or on Qatari subjects. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS). Pooled prevalence rates were calculated using the inverse variance weighting metaanalysis.

RESULTS

Out of 237 studies, 14 met our inclusion criteria, with a combined sample size of 14,772 from the Qatari population. The overall pooled prevalence of metabolic syndrome was 26%. The ATP III and IDF criteria exhibited significant differences in prevalence rates, with the IDF criteria showing a higher prevalence. Age ≥ 40 years demonstrated a higher prevalence compared to the younger group. Studies post-2018 reported a decreasing trend in metabolic syndrome prevalence.

CONCLUSION

The prevalence of metabolic syndrome in the Qatari population is comparable to rates in the Middle East. The study underscores the need for tailored interventions and strategies, especially targeting the older age group. Continuous research and monitoring are essential to track and understand the disease's progression in Qatar.

Evaluation status of current and emerging minimally invasive robotic surgical platforms

BACKGROUND

The rapid adoption of robotics within minimally invasive surgical specialties has also seen an explosion of new technology including multi- and single port, natural orifice transluminal endoscopic surgery (NOTES), endoluminal and "on-demand" platforms. This review aims to evaluate the validation status of current and emerging MIS robotic platforms, using the IDEAL Framework.

METHODS

A scoping review exploring robotic minimally invasive surgical devices, technology and systems in use or being developed was performed, including general surgery, gynaecology, urology and cardiothoracics. Systems operating purely outside the abdomen or thorax and endoluminal or natural orifice platforms were excluded. PubMed, Google Scholar, journal reports and information from the public domain were collected. Each company was approached via email for a virtual interview to discover more about the systems and to quality check data. The IDEAL Framework is an internationally accepted tool to evaluate novel surgical technology, consisting of four stages: idea, development/exploration, assessment, and surveillance. An IDEAL stage, synonymous with validation status in this review, was assigned by reviewing the published literature.

RESULTS

21 companies with 23 different robotic platforms were identified for data collection, 13 with national and/or international regulatory approval. Of the 17 multiport systems, 1 is fully evaluated at stage 4, 2 are stage 3, 6 stage 2b, 2 at stage 2a, 2 stage 1, and 4 at the pre-IDEAL stage 0. Of the 6 single-port systems none have been fully evaluated with 1 at stage 3, 3 at stage 1 and 2 at stage 0.

CONCLUSIONS

The majority of existing robotic platforms are currently at the preclinical to developmental and exploratory stage of evaluation. Using the IDEAL framework will ensure that emerging robotic platforms are fully evaluated with long-term data, to inform the surgical workforce and ensure patient safety.

Urolithiasis-related ureteric intussusception: A rare case report

Ureteral intussusception is a rare condition that historically occurs as a complication of ureteral neoplasms or iatrogenic endoscopic procedures. Although the exact mechanism of ureteral intussusception is unclear, most reported cases are due to leading points as malignant or benign masses. Urolithiasis related is rarely reported and can be challenging in stone management as it might decrease the spontaneous stone passage rate. In addition, it will increase the complexity of the endoscopic stone management. We present the second reported case of urolithiasis-related ureteric intussusception presented with urosepsis due to obstructive uropathy, successfully managed by an endourological approach.

The Parkinson's disease risk gene cathepsin B promotes fibrillar alpha-synuclein clearance, lysosomal function and glucocerebrosidase activity in dopaminergic neurons

Variants in the CTSB gene encoding the lysosomal hydrolase cathepsin B (catB) are associated with increased risk of Parkinson's disease (PD). However, neither the specific CTSB variants driving these associations nor the functional pathways that link catB to PD pathogenesis have been characterized. CatB activity contributes to lysosomal protein degradation and regulates signaling processes involved in autophagy and lysosome biogenesis. Previous in vitro studies have found that catB can cleave monomeric and fibrillar alpha-synuclein, a key protein involved in the pathogenesis of PD that accumulates in the brains of PD patients. However, truncated synuclein isoforms generated by catB cleavage have an increased propensity to aggregate. Thus, catB activity could potentially contribute to lysosomal degradation and clearance of pathogenic alpha synuclein from the cell, but also has the potential of enhancing synuclein pathology by generating aggregation-prone truncations. Therefore, the mechanisms linking catB to PD pathophysiology remain to be clarified. Here, we conducted genetic analyses of the association between common and rare CTSB variants and risk of PD. We then used genetic and pharmacological approaches to manipulate catB expression and function in cell lines and induced pluripotent stem cell-derived dopaminergic neurons and assessed lysosomal activity and the handling of aggregated synuclein fibrils. We find that catB inhibition impairs autophagy, reduces glucocerebrosidase (encoded by GBA1 ) activity, and leads to an accumulation of lysosomal content. In cell lines, reduction of CTSB gene expression impairs the degradation of pre-formed alpha-synuclein fibrils, whereas CTSB gene activation enhances fibril clearance. In midbrain organoids and dopaminergic neurons treated with alpha-synuclein fibrils, catB inhibition potentiates the formation of inclusions which stain positively for phosphorylated alpha-synuclein. These results indicate that the reduction of catB function negatively impacts lysosomal pathways associated with PD pathogenesis, while conversely catB activation could promote the clearance of pathogenic alpha-synuclein.

The gut microbiome in the fight against obesity: The potential of dietary factors

Obesity as a global public health burden has experienced a drastic growing trend recently. The management of obesity is challenging because of its complex etiology, and various factors are involved in its development, such as genetic and environmental factors. Different approaches are available to treat and/or manage obesity, including diet, physical activity, lifestyle changes, medications, and surgery. However, some of these approaches have inherent limitations and are closely associated with adverse effects. Therefore, probing into a novel/safe approach to treat and/or manage obesity is of fundamental importance. One such approach gaining renewed interest is the potential role of gut microbiota in obesity and its effectiveness in treating this condition. However, there is a dearth of comprehensive compilation of data on the potential role of the gut microbiome in obesity, particularly regarding dietary factors as a therapeutic approach. Therefore, this review aims to provide an updated overview of the role of gut microbiota in obesity, further highlighting the importance of dietary factors, particularly diet, prebiotics, and probiotics, as potential complementary and/or alternative therapeutic options. Moreover, the association of gut microbiota with obese or lean individuals has also been discussed.

Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review

PURPOSE OF REVIEW

This literature review aims to provide a comprehensive overview of the recent advances in prediction models and the deployment of AI and ML in the prediction of cardiopulmonary resuscitation (CPR) success. The objectives are to understand the role of AI and ML in healthcare, specifically in medical diagnosis, statistics, and precision medicine, and to explore their applications in predicting and managing sudden cardiac arrest outcomes, especially in the context of prehospital emergency care.

RECENT FINDINGS

The role of AI and ML in healthcare is expanding, with applications evident in medical diagnosis, statistics, and precision medicine. Deep learning is gaining prominence in radiomics and population health for disease risk prediction. There's a significant focus on the integration of AI and ML in prehospital emergency care, particularly in using ML algorithms for predicting outcomes in COVID-19 patients and enhancing the recognition of out-of-hospital cardiac arrest (OHCA). Furthermore, the combination of AI with automated external defibrillators (AEDs) shows potential in better detecting shockable rhythms during cardiac arrest incidents. AI and ML hold immense promise in revolutionizing the prediction and management of sudden cardiac arrest, hinting at improved survival rates and more efficient healthcare interventions in the future. Sudden cardiac arrest (SCA) continues to be a major global cause of death, with survival rates remaining low despite advanced first responder systems. The ongoing challenge is the prediction and prevention of SCA. However, with the rise in the adoption of AI and ML tools in clinical electrophysiology in recent times, there is optimism about addressing these challenges more effectively.

A Meta Modeling-Based Interoperability and Integration Testing Platform for IoT Systems

The rapid growth of the Internet of Things (IoT) and its integration into various industries has made it extremely challenging to guarantee IoT systems' dependability and quality, including scalability, dynamicity, and integration with existing IoT frameworks. However, the essential principles, approaches, and advantages of model-driven IoT testing indicate a promising strategy for overcoming these. This paper proposes a metamodeling-based interoperability and integration testing approach for IoT systems that automates the creation of test cases and the assessment of system performance by utilizing formal models to reflect the behavior and interactions of IoT systems. The proposed model-based testing enables the systematic verification and validation of complex IoT systems by capturing the essential characteristics of IoT devices, networks, and interactions. This study describes the key elements of model-driven IoT testing, including the development of formal models, methods for generating test cases, and the execution and assessment of models. In addition, it examines various modeling formalisms and their use in IoT testing, including state-based, event-driven, and hybrid models. This study examines several methods for creating test cases to ensure thorough and effective testing, such as constraint-based strategies and model coverage requirements. Model-driven IoT testing improves defect detection, expands test coverage, decreases testing effort, and increases system reliability. It also offers an organized and automated method to confirm the efficiency and dependability of IoT systems.

Association of Rare Variants in ARSA with Parkinson's Disease

BACKGROUND

Several lysosomal genes are associated with Parkinson's disease (PD), yet the association between PD and ARSA remains unclear.

OBJECTIVES

To study rare ARSA variants in PD.

METHODS

To study rare ARSA variants (minor allele frequency < 0.01) in PD, we performed burden analyses in six independent cohorts with 5801 PD patients and 20,475 controls, followed by a meta-analysis.

RESULTS

We found evidence for associations between functional ARSA variants and PD in four cohorts (P ≤ 0.05 in each) and in the meta-analysis (P = 0.042). We also found an association between loss-of-function variants and PD in the United Kingdom Biobank cohort (P = 0.005) and in the meta-analysis (P = 0.049). These results should be interpreted with caution as no association survived multiple comparisons correction. Additionally, we describe two families with potential co-segregation of ARSA p.E382K and PD.

CONCLUSIONS

Rare functional and loss-of-function ARSA variants may be associated with PD. Further replications in large case-control/familial cohorts are required. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Modelling and analysis of the complement system signalling pathways: roles of C3, C5a and pro-inflammatory cytokines in SARS-CoV-2 infection

The complement system is an essential part of innate immunity. It is activated by invading pathogens causing inflammation, opsonization, and lysis via complement anaphylatoxins, complement opsonin's and membrane attack complex (MAC), respectively. However, in SARS-CoV-2 infection overactivation of complement system is causing cytokine storm leading to multiple organs damage. In this study, the René Thomas kinetic logic approach was used for the development of biological regulatory network (BRN) to model SARS-CoV-2 mediated complement system signalling pathways. Betweenness centrality analysis in cytoscape was adopted for the selection of the most biologically plausible states in state graph. Among the model results, in strongly connected components (SCCs) pro-inflammatory cytokines (PICyts) oscillatory behaviour between recurrent generation and downregulation was found as the main feature of SARS-CoV-2 infection. Diversion of trajectories from the SCCs leading toward hyper-inflammatory response was found in agreement with in vivo studies that overactive innate immunity response caused PICyts storm during SARS-CoV-2 infection. The complex of negative regulators FI, CR1 and DAF in the inhibition of complement peptide (C5a) and PICyts was found desirable to increase immune responses. In modelling role of MAC and PICyts in lowering of SARS-CoV-2 titre was found coherent with experimental studies. Intervention in upregulation of C5a and PICyts by C3 was found helpful in back-and-forth variation of signalling pattern linked with the levels of PICyts. Moreover, intervention in upregulation of PICyts by C5a was found productive in downregulation of all activating factors in the normal SCCs. However, the computational model predictions require experimental studies to be validated by exploring the activation role of C3 and C5a which could change levels of PICyts at various phases of SARS-CoV-2 infection.

Migrated Hem-o-lok clip in the bladder: A rare cause of bladder stone for cystolitholapaxy

The bladder is the most common site of foreign bodies in the urinary tract. Most foreign bodies are self-inserted via the urethra due to exotic impulses, psychometric problems, or sexual curiosity. Here we present a rare case of bladder stones due to the migration of the Heme-o-lok clip. We present a case of a 76-year-old male with hematuria for 4 days. An abdominal computed tomography (CT) scan showed a 15 mm calculus noted in the urinary bladder. The patient underwent cystolitholapaxy which was successful. Foreign bodies inserted in the bladder pose a significant challenge and require timely intervention.

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning

The COVID-19 pandemic has presented a unique challenge for physicians worldwide, as they grapple with limited data and uncertainty in diagnosing and predicting disease outcomes. In such dire circumstances, the need for innovative methods that can aid in making informed decisions with limited data is more critical than ever before. To allow prediction with limited COVID-19 data as a case study, we present a complete framework for progression and prognosis prediction in chest X-rays (CXR) through reasoning in a COVID-specific deep feature space. The proposed approach relies on a pre-trained deep learning model that has been fine-tuned specifically for COVID-19 CXRs to identify infection-sensitive features from chest radiographs. Using a neuronal attention-based mechanism, the proposed method determines dominant neural activations that lead to a feature subspace where neurons are more sensitive to COVID-related abnormalities. This process allows the input CXRs to be projected into a high-dimensional feature space where age and clinical attributes like comorbidities are associated with each CXR. The proposed method can accurately retrieve relevant cases from electronic health records (EHRs) using visual similarity, age group, and comorbidity similarities. These cases are then analyzed to gather evidence for reasoning, including diagnosis and treatment. By using a two-stage reasoning process based on the Dempster-Shafer theory of evidence, the proposed method can accurately predict the severity, progression, and prognosis of a COVID-19 patient when sufficient evidence is available. Experimental results on two large datasets show that the proposed method achieves 88% precision, 79% recall, and 83.7% F-score on the test sets.

Association of rare variants in ARSA with Parkinson's disease

Background

Several lysosomal genes are associated with Parkinson's disease (PD), yet the association between PD and ARSA , which encodes for the enzyme arylsulfatase A, remains controversial.

Objectives

To evaluate the association between rare ARSA variants and PD.

Methods

To study possible association of rare variants (minor allele frequency<0.01) in ARSA with PD, we performed burden analyses in six independent cohorts with a total of 5,801 PD patients and 20,475 controls, using optimized sequence Kernel association test (SKAT-O), followed by a meta-analysis.

Results

We found evidence for an association between functional ARSA variants and PD in four independent cohorts (P≤0.05 in each) and in the meta-analysis (P=0.042). We also found an association between loss-of-function variants and PD in the UKBB cohort (P=0.005) and in the meta-analysis (P=0.049). However, despite replicating in four independent cohorts, these results should be interpreted with caution as no association survived correction for multiple comparisons. Additionally, we describe two families with potential co-segregation of the ARSA variant p.E384K and PD.

Conclusions

Rare functional and loss-of-function ARSA variants may be associated with PD. Further replication in large case-control cohorts and in familial studies is required to confirm these associations.

NPC1 variants are not associated with Parkinson’s disease, REM-sleep behaviour disorder or Dementia with Lewy bodies in European cohorts

NPC1 encodes a lysosomal protein involved in cholesterol transport. Biallelic mutations in this gene may lead to Niemann-Pick disease type C (NPC), a lysosomal storage disorder. The role of NPC1 in alpha synucleinopathies is still unclear, as different genetic, clinical, and pathological studies have reported contradictory results. This study aimed to evaluate the association of NPC1 variants with the synucleinopathies Parkinson's disease (PD), dementia with Lewy bodies (DLB), and rapid eye movement-sleep behavior disorder (RBD). We analyzed common and rare variants from 3 cohorts of European descent: 1084 RBD cases and 2945 controls, 2852 PD cases and 1686 controls, and 2610 DLB cases and 1920 controls. Logistic regression models were used to assess common variants while optimal sequence Kernel association tests were used to assess rare variants, both adjusted for sex, age, and principal components. No variants were associated with any of the synucleinopathies, supporting that common and rare NPC1 variants do not play an important role in alpha synucleinopathies.

Aesthetic Refinements in Male Chest Lifting

The authors describe aesthetic refinements to the approach for male chest lifting in male patients with grade 3 gynecomastia and/or significant chest skin excess. An inferior pedicle is used to transpose the nipple-areolar complex allowing preservation of pigment and sensation, liposuction and direct excision are used to reduce volume and excess skin, and the resulting curvilinear scar along the inferior and lateral border of the chest provide a more masculine appearance. Early experience with this technique has shown it to be safe and effective. Perioperative management and the detailed steps of the procedure are outlined.

Assessing the Effectiveness of Clinical Skills Laboratory and Traditional Lecture in Teaching Basic Life Support and Performance Evaluation According to Different Domains of Revised Bloom's Taxonomy

Sudden cardiac arrest out-side hospital is serious global concern. If non-medical people are taught to initiate the basic life support (BLS) training with cardiopulmonary resuscitation (CPR) then the mortality could be reduced significantly. This was a non-randomized controlled study to evaluate clinical skills laboratory (CSL) as teaching tool for basic life support (BLS) training in comparison to traditional lecture. Sample size was 68 and performed in Sylhet Women's Medical College from July 2022 to September 2022. All the participants were third year nursing students. They were enrolled in to two groups. Group-A were taught BLS by clinical skills laboratory (CSL) and Group-B were taught by traditional lecture (TL). At the end of the teaching all of them were tested by a vetted multiple choice question (MCQ) set. The questions were set according the 5 levels of revised Blood's taxonomy. Mean score of Group-A (CSL) were higher the TL group (p=0.0003). Among the revised Bloom's taxonomy understand, apply and evaluate domains were significantly better taught (p<0.05) by CSL. The sensitivity of CSL was 0.559 in comparison to TL for BLS training. In the modern medical education teaching and assessment should be focused on the higher levels of learning taxonomy. Introducing CSL in medical education could boost up the psychomotor and cognition both in the medical education.

The International Natural Product Sciences Taskforce (INPST) and the power of Twitter networking exemplified through #INPST hashtag analysis

BACKGROUND

The development of digital technologies and the evolution of open innovation approaches have enabled the creation of diverse virtual organizations and enterprises coordinating their activities primarily online. The open innovation platform titled "International Natural Product Sciences Taskforce" (INPST) was established in 2018, to bring together in collaborative environment individuals and organizations interested in natural product scientific research, and to empower their interactions by using digital communication tools.

METHODS

In this work, we present a general overview of INPST activities and showcase the specific use of Twitter as a powerful networking tool that was used to host a one-week "2021 INPST Twitter Networking Event" (spanning from 31st May 2021 to 6th June 2021) based on the application of the Twitter hashtag #INPST.

RESULTS AND CONCLUSION

The use of this hashtag during the networking event period was analyzed with Symplur Signals (https://www.symplur.com/), revealing a total of 6,036 tweets, shared by 686 users, which generated a total of 65,004,773 impressions (views of the respective tweets). This networking event's achieved high visibility and participation rate showcases a convincing example of how this social media platform can be used as a highly effective tool to host virtual Twitter-based international biomedical research events.

Estimation of a cut-off value for immature platelet fraction (IPF) in predicting platelet recovery in dengue patients with thrombocytopenia

INTRODUCTION

Thrombocytopenia is a common complication in dengue that sometimes necessitates platelet transfusion. Immature platelet fraction (IPF) measures immature platelets that indirectly reflect thrombopoiesis and is helpful in predicting platelet recovery.

OBJECTIVES

This study aimed to evaluate the role of IPF% and identify its cut-off value in predicting platelet recovery in dengue patients with thrombocytopenia.

MATERIALS AND METHODS

Serial platelet count and IPF results were obtained from fifty-four confirmed dengue patients with platelet count <50x109 /L. Median peak IPF% and number of patients with platelet recovery were determined. Receiver operating characteristic (ROC) curve is generated to identify the IPF% cut-off value to predict platelet recovery.

RESULTS

Median peak IPF% among dengue patients was 12.15% with 83.3% of them achieving platelet recovery after reaching the peak IPF%. There was a significant difference between median IPF% on day one of admission with peak IPF% among dengue patients. ROC curve analysis showed IFP% of 10.55% can be used to predict platelet recovery with a sensitivity of 69% and a specificity of 67%.

CONCLUSION

IPF% is a reliable and useful parameter in predicting platelet recovery in dengue patients. This would assist the clinician in managing dengue patients especially those with severe thrombocytopenia without giving unnecessary platelet transfusion.

A Canadian Experience With Off-the-Shelf, Aseptically Processed, Costal Cartilage Segment Allografts in Complex Rhinoplasty

Background

Complex primary and secondary rhinoplasties usually necessitate grafting materials when native nasal cartilage is inadequate for reconstruction. Fresh frozen, aseptically processed, and nonterminally sterilized costal cartilage segment allografts (CCSAs) are a novel grafting material for such cases that avoid donor-site morbidity, improve operating efficiency, and mitigate the postoperative risks.

Objectives

To report the early experience using fresh frozen, aseptically processed, and nonterminally sterilized CCSAs used in complex primary and secondary rhinoplasties, in Canada.

Methods

We retrospectively reviewed 21 patients (17 female and 4 male patients) who underwent a primary or secondary rhinoplasty surgery using CCSAs from June 2019 to April 2022.

Results

The mean age was 39 years (range, 27-58 years), and the mean body mass index was 23.7 kg/m² (range, 24-40 kg/m²). Of the 21 procedures, 11 were primary (52.4%) and 10 were secondary (47.6%) rhinoplasties. The mean operative time was 185 min (range, 85-330 min), with a mean follow-up time of 15.0 months (range, 2.0-37.8 months). At follow-up, 19 patients (90.5%) reported being "very satisfied" with their aesthetic results, and only 2 (9.5%) underwent revision surgery. No serious complications were reported, and only 1 case showed evidence of graft resorption.

Conclusions

Based on early experience, this CCSA avoids donor-site morbidity and reduces operative time while maintaining a low complication rate, providing a viable alternative to the use of autologous costal cartilage when indicated in complex primary or secondary rhinoplasties with inadequate native nasal cartilage.

Level of Evidence 4

GALC variants affect galactosylceramidase enzymatic activity and risk of Parkinson’s disease

The association between glucocerebrosidase (GCase), encoded by GBA, and Parkinson’s disease highlights the role of the lysosome in Parkinson’s disease pathogenesis. Genome-wide association studies (GWAS) in Parkinson’s disease have revealed multiple associated loci, including the GALC locus on chromosome 14. GALC encodes the lysosomal enzyme galactosylceramidase (GalCase), which plays a pivotal role in the glycosphingolipid metabolism pathway. It is still unclear whether GALC is the gene driving the association in the chromosome 14 locus, and if so, by which mechanism.

We first aimed to examine whether variants in the GALC locus and across the genome are associated with GalCase activity. We performed a GWAS in two independent cohorts from a)Columbia University and b)the Parkinson’s Progression Markers Initiative study, followed by a meta-analysis with a total of 976 Parkinson’s disease patients and 478 controls with available data on GalCase activity. We further analyzed the effects of common GALC variants on expression and GalCase activity using genomic colocalization methods. Mendelian randomization was used to study whether GalCase activity may be causal in Parkinson’s disease. To study the role of rare GALC variants we analyzed sequencing data from 5,028 Parkinson’s disease patients and 5,422 controls. Additionally, we studied the functional impact of GALC knock-out on alpha-synuclein accumulation and on GCase activity in neuronal cell models and performed in silico structural analysis of common GALC variants associated with altered GalCase activity.

The top hit in Parkinson's disease GWAS in the GALC locus, rs979812, is associated with increased GalCase activity (b = 1.2; se = 0.06; p = 5.10E-95). No other variants outside the GALC locus were associated with GalCase activity. Colocalization analysis demonstrated that rs979812 was also associated with increased GalCase expression. Mendelian randomization suggested that increased GalCase activity may be causally associated with Parkinson’s disease (b = 0.025, se = 0.007, p = 0.0008). We did not find an association between rare GALC variants and Parkinson’s disease. GALC knockout using CRISPR-Cas9 did not lead to alpha-synuclein accumulation, further supporting that increased rather than reduced GalCase levels may be associated with Parkinson’s disease. The structural analysis demonstrated that the common variant p.I562T may lead to improper maturation of GalCase affecting its activity.

Our results nominate GALC as the gene associated with Parkinson’s disease in this locus and suggest that the association of variants in the GALC locus may be driven by their effect of increasing GalCase expression and activity. Whether altering GalCase activity could be considered as a therapeutic target should be further studied.

Functional recoverability post-pyeloplasty in children with ureteropelvic junction obstruction and poorly functioning kidneys: Systematic review

BACKGROUND

The management of poorly functioning kidneys (PFK) associated with ureteropelvic junction obstruction (UPJO) is controversial. There is contradictory information about how to best manage these cases: pyeloplasty or nephrectomy?

OBJECTIVE

To systematically summarize the available evidence concerning the effects of pyeloplasty on the differential renal function of PFK in children with unilateral UPJO, highlighting the ongoing challenges in their definition, management, and long-term follow-up. In addition, we aim to verify potential predictors of renal functional recoverability that could help clinicians choose candidates for pyeloplasty.

METHODS

We searched several databases including PubMed, Embase, and Cochrane Library CENTRAL until August 20, 2021, according to the PRISMA guidelines. The following concepts were searched: pediatric, ureteropelvic junction obstruction, UPJO, pyeloplasty, recovery, split renal function, and differential renal function. We enrolled studies where the PFK was defined as preoperative differential renal function (DRF) ≤30% by renal scintigraphy. Potential predictors of renal functional recoverability were assessed and compared among studies. The quality of the included studies was evaluated using a modified version of the Newcastle-Ottawa scale (NOS).

RESULTS

1499 citations perceived as relevant to screening were retrieved. After screening, 20 studies were included, comprising a total of 625 cases. The number of patients in each study varied between 5 and 84, while the average post-surgical follow-up duration ranged between 3 months and 180 months. The most significant preoperative predictive factor for postoperative functional recoverability was the baseline DRF, especially when antenatally diagnosed. The quality was considered average in a significant portion of included studies.

CONCLUSION

A significant proportion of PFK showed an increase of DRF post-pyeloplasty. However, no consistent predictive factors for functional recoverability have yet been determined apart from preoperative DRF. Until further evidence appears, pyeloplasty should be considered a valid option in the armamentarium of UPJO management in PFK.

License plate identification and recognition in a non-standard environment using neural pattern matching

Non-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

Customer's decision and affective assessment of online product recommendation: A recommendation-product congruity proposition

Online product recommendation (OPR) systems have gained prominence in the context of e-commerce over the past years. Despite the increased research on OPR use, less attention has been paid to examining how decision and affective assessment of the OPR are contingent upon the product type. This study proposes and examines a recommendation-product congruity proposition based on cognitive fit and schema congruity theories. The proposition states that when the content (i.e., a stimulus-based schema) of the OPR [either system-generated recommendation (SGR) or a consumer-generated recommendation (CGR)] matches the brain-stored schema initiated by a particular product (either a search product or an experienced product), then a consumer would use a schema-based information assessment strategy and experience favorable decision and affective assessment of the OPR. This then affects consumers' intentions to purchase and reuse OPR. The proposition is tested via a 2 × 2 between-respondents factorial design of a cross-sectional survey with 482 Amazon customers. The results support the following two matching conditions of the proposition: (1) SGR describing a search product and (2) CGR explaining an experienced product, which might lead customers to perceive lower decision effort, greater decision quality, and higher enjoyment with the OPR that subsequently have a significant impact on their intentions to purchase and reuse OPR. This study expands our understanding of how recommendation-product congruence influences the consumer's decision and affective assessment behavior and provides practical implications for the identification and presentation of the recommendation type and product type for a better customer decision.

System level modeling and analysis of TNF-α mediated sphingolipid signaling pathway in neurological disorders for the prediction of therapeutic targets

Sphingomyelin (SM) belongs to a class of lipids termed sphingolipids. The disruption in the sphingomyelin signaling pathway is associated with various neurodegenerative disorders. TNF-α, a potent pro-inflammatory cytokine generated in response to various neurological disorders like Alzheimer’s disease (AD), Parkinson’s disease (PD), and Multiple Sclerosis (MS), is an eminent regulator of the sphingomyelin metabolic pathway. The immune-triggered regulation of the sphingomyelin metabolic pathway via TNF-α constitutes the sphingomyelin signaling pathway. In this pathway, sphingomyelin and its downstream sphingolipids activate various signaling cascades like PI3K/AKT and MAPK/ERK pathways, thus, controlling diverse processes coupled with neuronal viability, survival, and death. The holistic analysis of the immune-triggered sphingomyelin signaling pathway is imperative to make necessary predictions about its pivotal components and for the formulation of disease-related therapeutics. The current work offers a comprehensive in silico systems analysis of TNF-α mediated sphingomyelin and downstream signaling cascades via a model-based quantitative approach. We incorporated the intensity values of genes from the microarray data of control individuals from the AD study in the input entities of the pathway model. Computational modeling and simulation of the inflammatory pathway enabled the comprehensive study of the system dynamics. Network and sensitivity analysis of the model unveiled essential interaction parameters and entities during neuroinflammation. Scanning of the key entities and parameters allowed us to determine their ultimate impact on neuronal apoptosis and survival. Moreover, the efficacy and potency of the FDA-approved drugs, namely Etanercept, Nivocasan, and Scyphostatin allowed us to study the model’s response towards inhibition of the respective proteins/enzymes. The network analysis revealed the pivotal model entities with high betweenness and closeness centrality values including recruit FADD, TNFR_TRADD, act CASP2, actCASP8, actCASP3 and 9, cytochrome C, and RIP_RAIDD which profoundly impacted the neuronal apoptosis. Whereas some of the entities with high betweenness and closeness centrality values like Gi-coupled receptor, actS1PR, Sphingosine, S1P, actAKT, and actERK produced a high influence on neuronal survival. However, the current study inferred the dual role of ceramide, both on neuronal survival and apoptosis. Moreover, the drug Nivocasan effectively reduces neuronal apoptosis via its inhibitory mechanism on the caspases.

Current Practices in Dorsal Augmentation Rhinoplasty

BACKGROUND

Dorsal augmentation rhinoplasty addresses the aesthetic and functional impairments caused by a deficient nasal dorsum. Augmentation rhinoplasty can be performed using a variety of different surgical techniques and grafting materials that all have distinct advantages and disadvantages.

METHODS

Grafting materials have unique characteristics, uses, and safety profiles. A detailed overview of various grafting materials and their uses, risks, and benefits is provided.

RESULTS

Autologous grafting materials include septal cartilage, auricular cartilage, and costal cartilage. These donor sites can provide various amounts of en bloc or diced cartilage. Alternatively, bone may be used when strong structural stability is required, and soft tissue may be used to fill mild to moderate defects. Homologous grafts (e.g., irradiated and nonirradiated rib) and acellular dermal matrices are alternatives to autologous graft with many similar advantages and no need for an additional surgical site. Lastly, alloplastic implants may be successfully used for dorsal augmentation if both patient and surgeon understand their associated risks.

CONCLUSION

To perform successful dorsal augmentation, surgeons should be familiar with the wide variety of operative approaches and augmentation materials that are currently available and understand their risks, benefits, and uses.

Effect of Cardiopulmonary Bypass on Blood and Coagulation Profile in Patients Undergoing Cardiac Surgery

This study was carried out in the department of cardiac surgery, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh from July 2014 to April 2016. This study aims to evaluate the inadvertent effect of cardiopulmonary bypass on complete blood count and coagulation profile. This study was also compared the hemostatic parameters between patients undergoing elective cardiac surgery with CPB and without CPB. A total of 55 patients were included in this study. Among them 20 patients (Group A) were selected for elective cardiac surgery without cardiopulmonary bypass (CPB), 20 patients (Group B) with cardiopulmonary bypass time less than 90 minutes and 15 patients (Group C) were included with cardiopulmonary bypass time either 90 minutes or more. The mean age were 51.5±4.7 years ranging from 40-57 years in Group A, 33.2±10.2 years ranging from 18-50 years in Group B and 34.2±11.4 years ranging from 18-57 years in Group C. The difference of age was statistically significant (p<0.05) among three groups. The difference of post-operative mean hemoglobin and RBC value, WBC and Platelet count on arrival at the intensive care unit, at 48 hours and at 7 days after surgery were statistically significant (p<0.05) in the three groups. However, on arrival at Intensive Care Unit, after 48 hours and at 7 days after surgery, the change of coagulation profile like mean fibrinogen level, bleeding time, clotting time and prothrombin time were statistically significant (p<0.05) among the groups. Patient with long cardiopulmonary bypass time had shown blood and coagulation profile abnormality and it can be minimized if we can curtail the bypass time.

A video summarization framework based on activity attention modeling using deep features for smart campus surveillance system

Like other business domains, digital monitoring has now become an integral part of almost every academic institution. These surveillance systems cover all the routine activities happening on the campus while producing a massive volume of video data. Selection and searching the desired video segment in such a vast video repository is highly time-consuming. Effective video summarization methods are thus needed for fast navigation and retrieval of video content. This paper introduces a keyframe extraction method to summarize academic activities to produce a short representation of the target video while preserving all the essential activities present in the original video. First, we perform fine-grain activity recognition using a realistic Campus Activities Dataset (CAD) by modeling activity attention scores using a deep CNN model. In the second phase, we use the generated attention scores for each activity category to extract significant video frames. Finally, we evaluate the inter-frame similarity index used to reduce the number of redundant frames and extract only the representative keyframes. The proposed framework is tested on different videos, and the experimental results show the performance of the proposed summarization process.

Biodiversity, distributions and isolation of microplastics pollution in finfish species in the Panjkora River at Lower and Upper Dir districts of Khyber Pakhtunkhwa province of Pakistan

The basic aim of this study was aimed to determine the ichthyofaunal diversity of River Panjkora in both upper and lower Dir districts in Khyber Pakhtunkhwa province of Pakistan.Fish samples were collected by using fishnets from March to September 2020. A total of 724 specimens were collected and classified into 5 families, 14 genera, and 18 species. The overall results revealed that most fish fauna of river Panjkora contains 8 species of family Cyprinidae (56.49%) followed by 4 species of Nemacheilidae (24.44%), 2 species of Channidae (10.63%), and Sisoridae (7.04%), and 1 species of Mastacembelidae (1.38%), respectively. Among all kinds of fish species, Schizothorax plagiostomus (16.57%) was highly dominated and followed by Carassius auratus (11.87%) and Racoma labiata (9.66%) and were reported as highly abundant, especially during April, May, and June. The least abundant species were Glyptothorax punjabensis, Glyptothorax sufii, and Mastacembelus armatus, that constituting 2.48%, 2.20%, and 1.38% of the total fish samples. The Overall Simpson's diversity (1-D= 0.919) and Simpson's Reciprocal index values (1/D= 12.3876), and Shannon's index (H= 2.68) were indicating that river Panjkora contains a quite rich and diverse group of fish species. The highest microplastics observed in site 7 compared to other study area. Conservation steps should be taken as a top priority to protect and conserve the marine environment and natural heritage from further loss, extinction and stop or minimize losses incurred through irresponsible fishery practices.

Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans

The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.

Vertical Scar Mastopexy With a Centrally Based Auto-Augmentation Flap

The authors describe their technique for vertical scar mastopexy with a centrally based auto-augmentation flap. Since 2011, the authors have performed this procedure in 212 patients and found that this operative technique has allowed us to achieve reproducible aesthetic outcomes while minimizing complications. Vertical scar mastopexy with a centrally based auto-augmentation flap is an excellent procedure for patients with breast ptosis who desire improved breast shape and superior pole fullness without the use of a breast implant. The perioperative management and detailed steps of the procedure are outlined.

Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

The Safe Resumption of Elective Plastic Surgery in Accredited Ambulatory Surgery Facilities During the COVID-19 Pandemic

Background

On March 11, 2020 the World Health Organization declared COVID-19 a worldwide pandemic resulting in an unprecedented shift in the Canadian health care system, where protection of an already overloaded health care system became a priority; all elective surgeries and non-essential activities were ceased. With the impact being less than predicted, on May 26, 2020, elective surgeries and non-essential activities were permitted to resume.

Objectives

To examine outcomes following elective aesthetic surgery and the impact on the Canadian health care system with the resumption of these services during the COVID-19 worldwide pandemic.

Methods

Data was collected in a prospective manner on consecutive patients undergoing elective plastic surgery procedures in six accredited ambulatory surgery facilities. Data included patient demographics, procedural characteristics, COVID-19 PCR test status, airway management and postoperative outcomes.

Results

368 patients underwent elective surgical procedures requiring a general anesthetic. All 368 patients that underwent surgery were negative on pre visit screening. A COVID-19 PCR test was completed by 352 patients (95.7%) and all were negative. In the postoperative period, seven patients (1.9%) had complications, three patients (0.8%) required a hospital visit, and one patient (0.3%) required hospital admission. No patients or health care providers developed COVID-19 symptoms or had a positive test for COVID-19 within 30 days of surgery.

Conclusions

With appropriate screening and safety precautions, elective aesthetic plastic surgery can be performed in a manner that is safe for patients and health care providers and with a very low risk for accelerating virus transmission within the community.

639 Emergency Surgical Approach to A Bleeding Pseudoaneurysm Of the Posterior Inferior Pancreaticoduodenal Artery After Failed Embolisation

Background

Pseudoaneurysms of visceral arteries are uncommon, with a prevalence of 0.01% to 0.2%1. Initial treatment is radiological embolisation (RE)2. If this fails, surgical access to the pseudoaneurysm is extremely difficult. They are associated with major complications such as rupture, ischaemia and shock2, with a 20-70% mortality rate3. We report the case of a patient admitted with a life-threatening bleed from an aberrant inferior pancreaticoduodenal artery (IPDA) with failed RE.

Case Summary

A 44-year-old patient presented with right upper quadrant pain, shock and low haemoglobin. His computerised tomography (CT) scan showed a large retroperitoneal haematoma with active bleeding from a 5x6mm IPDA pseudoaneurysm. After resuscitation, access to the IPDA during RE via the coeliac axis and superior mesenteric artery was unsuccessful and he deteriorated. He underwent an emergency laparotomy, which showed the retro-peritoneal haematoma had ruptured into the peritoneal cavity through the transverse mesocolon. Four-quadrant abdominal packing and supra-coeliac manual compression of the aorta was utilised. The right colon was mobilised with full kocherisation of the duodenum. The gastrocolic omentum was divided to enter the lesser sac. The haematoma was evacuated and bleeding branches from the IPDA were suture ligated. He required 26 units of blood throughout admission and underwent 24-hour damage control laparostomy on intensive care. CT mesenteric angiogram post-surgery and re-look laparotomy showed no further active bleeds. He had an uneventful recovery and discharged safely.

Conclusions

RE of visceral artery pseudoaneurysms is challenging. Surgery remains the last resort and should be performed by appropriately trained specialist surgeons.

A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection

Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous endeavours have been made for the improvements of medical disease detection, because the errors and problems in medical disease detection cause serious wrong medical treatment. Meta-heuristic techniques have been frequently utilized for the detection of medical diseases and promise better accuracy of perception and prediction of diseases in the domain of biomedical. Particle Swarm Optimization (PSO) is a swarm-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm during the searching of their food source. Consequently, for the versatility of numerical experimentation, PSO has been mostly applied to address the diverse kinds of optimization problems. However, the PSO techniques are frequently adopted for the detection of diseases but there is still a gap in the comparative survey. This paper presents an insight into the diagnosis of medical diseases in health care using various PSO approaches. This study presents to deliver a systematic literature review of current PSO approaches for knowledge discovery in the field of disease detection. The systematic analysis discloses the potential research areas of PSO strategies as well as the research gaps, although, the main goal is to provide the directions for future enhancement and development in this area. This paper gives a systematic survey of this conceptual model for the advanced research, which has been explored in the specified literature to date. This review comprehends the fundamental concepts, theoretical foundations, and conventional application fields. It is predicted that our study will be beneficial for the researchers to review the PSO algorithms in-depth for disease detection. Several challenges that can be undertaken to move the field forward are discussed according to the current state of the PSO strategies in health care.

Evaluating the Quality of Systematic Reviews and Meta-Analyses About Breast Augmentation Using AMSTAR

BACKGROUND

Breast augmentation is one of the most commonly performed cosmetic surgeries worldwide. Therefore, it is imperative to have evidence with high methodological quality to guide clinical decision making.

OBJECTIVES

To evaluate the methodological quality of the systematic reviews (SRs) focused on breast augmentation.

METHODS

A comprehensive search of MEDLINE, Embase, and the Cochrane Library of Systematic Reviews was performed. SRs that have a particular focus on breast augmentation and were published in the top 15 plastic and reconstructive surgery journals were included. Quality assessment was performed using a measurement tool to assess systematic reviews (AMSTAR). Study characteristics were extracted including journal and impact factor, year of publication, country affiliation of the corresponding author, reporting adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, number of citations, and number of studies included.

RESULTS

Among the 22 studies included for analysis, the mean AMSTAR score was moderate (5.55), with no SR achieving good quality (AMSTAR score of ≥9). There were no significant associations between AMSTAR score and journal impact factor, number of citations, year of publication, or number of included studies. Studies that reported adherence to PRISMA guidelines on average scored higher on the AMSTAR tool (P = 0.03).

CONCLUSIONS

The methodological quality of reviews about breast augmentation was found to be moderate, with no significant increase in studies or quality over time. Adherence to PRISMA guidelines and increased appraisal of SRs about breast augmentation using methodological assessment tools would further strengthen methodological quality and confidence in study findings.

Swarm Intelligence-Based Uplink Power Control in Cognitive Internet of Things (CIoT) for Underlay Environment

Internet of things (IoT) aims to shift intelligence to things and tends to increase the spectrum utilization efficiency. However, in doing so, it might generate high interference to the primary users (PUs) due to massive data flow into the networks. Cognitive radio smartly addresses this challenge by enabling different spectrum sharing modes while guaranteeing the quality of service. Motivated by this fact, the incorporation of cognitive abilities in IoT has given birth to a new sub-domain in IoT, known as Cognitive IoT (CIoT). This paper considers a single cell scenario in which multiple CIoT users (CUs) coexist with a PU in an underlay environment, and their communication performance has been optimized while adhering to the transmit power and interference constraints. Furthermore, two swarm intelligence-based implementations of the proposed algorithm have been provided, one based on Artificial Bee Colony (ABC) and the other based on Particle Swarm Optimization (PSO), and their effectiveness to solve the constrained power allocation problem for CIoT networks has been proved through simulations.

Prevalence of rhabdochoniasis in snow trout of river Swat and river Panjkora, Khyber Pakhtunkhwa province, Pakistan

This research was aimed to explore the helminth parasitic diseases in Schizothorax plagiostomus (the snow trout) from river Swat and river Panjkora, Pakistan. Collection of 360 fish specimens have done from the lower, middle and upper reaches of both the rivers through gill nets, cast nets, dragon nets and hooks. All the samples were examined in the University of Malakand, Zoology Department for helminth parasites during the months from January 2015 to December 2016. Of the total examined fish samples 21.9% (n=79) were infected with Rhabdochona spp including 17.7% (n=32/180) in river Swat and 26.6% (n=47/180) in river Panjkora. Highest month-wise prevalence (p=0.09878,p<0.05) was reported in May (30%), then by February and October each (26.6%) while the lowest during August (13.3%). Highest prevalence (p=0.9723, p<0.05) was reported in summer season (26.6%), while lowest in the winter season (20%). Adults were highly infected (p=< 0.0001) in prevalence (63.7%) followed by sub-adults (13.2%) while no infection was found in juvenile specimens. Females fish samples had higher (p=0.0277, p>0.05) prevalence (28.8%) than males (16.6%). Fishes of the lower reaches had highest (p=0.0029, P>0.05) prevalence (31.7%) followed by middle reaches (16.5%) while the lowest prevalence was observed in samples of fish collected from upper reaches (9.87%). Present study address that Rhabdochona spp in the intestine of snow trout has a long term relationship and call as a natural infection in cyprinids and zoonotic risk to human.

Long non-coding RNAs and their targets as potential biomarkers in breast cancer

Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease-specific biomarkers. Recently, long non-coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post-transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co-expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co-expressed lncRNAs and their cis- and trans-regulating mRNA targets which include RP11-108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.

Anti-Neuroinflammatory Potential of Polyphenols by Inhibiting NF-κB to Halt Alzheimer's Disease

Alzheimer's disease (AD) is an irrevocable chronic brain disorder featured by neuronal loss, microglial accumulation, and progressive cognitive impairment. The proper pathophysiology of this life-threatening disorder is not completely understood and no exact remedies have been found yet. Over the last few decades, research on AD has mainly highlighted pathomechanisms linked to a couple of the major pathological hallmarks, including extracellular senile plaques made of amyloid-β (Aβ) peptides, and intracellular neurofibrillary tangles (NFTs) made of tau proteins. Aβ can induce apoptosis, trigger an inflammatory response, and inhibit the synaptic plasticity of the hippocampus, which ultimately contributes to reducing cognitive functions and memory impairment. Recently, a third disease hallmark, the neuroinflammatory reaction that is mediated by cerebral innate immune cells, has become a spotlight in the current research area, assured by pre-clinical, clinical, and genetic investigations. Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), a cytokine producer, is significantly associated with physiological inflammatory proceedings and thus shows a promising candidate for inflammation- based AD therapy. Recent data reveal that phytochemicals, mainly polyphenol compounds, exhibit potential neuroprotective functions and these may be considered as a vital resource for discovering several drug candidates against AD. Interestingly, phytochemicals can easily interfere with the signaling pathway of NF-κB. This review represents the anti-neuroinflammatory potential of polyphenols as inhibitors of NF-κB to combat AD pathogenesis.