Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.

Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.

Targeted treatment for biofilm-based infections using PEGylated tobramycin.

Chronic infections often involve biofilm-based bacteria, in which the biofilm results in significant resistance against antimicrobial agents and prevents eradication of the infection. The physicochemical barrier presented by the biofilm matrix is a major impediment to the delivery of many antibiotics. Previously, PEGylation has been shown to improve antibiotic penetration into biofilms in vitro. In these studies, PEGylating tobramycin was investigated both in vitro and in vivo. Two distinct PEGylated tobramycin molecules were synthesized (mPEG-SA-Tob and mPEG-AA-Tob). Then, in a P. aeruginosa biofilm in vitro model, we found that mPEG-SA-Tob can operate as a prodrug and showed 7 times more effectiveness than tobramycin (MIC80: 14 µM vs.100 µM). This improved biofilm eradication is attributable to the fact that mPEG-SA-Tob can aid tobramycin to penetrate through the biofilm and overcome the alginate-mediated antibiotic resistance. Finally, we used an in vivo biofilm-based chronic pulmonary infection rat model to confirm the therapeutic impact of mPEG-SA-Tob on biofilm-based chronic lung infection. mPEG-SA-Tob has a better therapeutic impact than tobramycin in that it cannot only stop P. aeruginosa from multiplying in the lungs but can also reduce inflammation caused by infections and prevent a recurrence infection. Overall, our findings show that PEGylated tobramycin is an effective treatment for biofilm-based chronic lung infections.

The outcomes and complications of percutaneous interventions in chronic total coronary occlusion.

BACKGROUND: The limited availability of complex coronary intervention facilities and qualified operators, due to the high cost associated with chronic total occlusion (CTO) percutaneous intervention (PCI) equipment and a shortage of necessary skills, has led to a scarcity of capable medical centers in Pakistan. This study seeks to examine the outcomes and potential complications associated with CTO PCI procedures conducted at the Cardiac Catheterization Laboratories of a prominent national institute in Pakistan, which handles a large volume of cases. RESULTS: Three hundred and six patients were included in the study in the study period of six months. The mean age was 59.49 (± 9.16) years: 256 (83.66%) were male and 50 (16.34%) were female. CTO was successfully re-vascularized in 237 (77.5%) with a complication rate of 13.7%. Two hundred and ninety-eight (97.39%) patients underwent an antegrade approach, while RCA was the most common target vessel (47.71%). Diabetes was the only significant associated risk factor with CTO PCI failure (30.43% vs. 30.43%, P-value = 0.015). CONCLUSION: We achieved an excellent procedural success rate with a low complication rate. CTO procedural failure is associated with a higher complication rate, and diabetes is among the risk factors that lead to higher procedural failure.

The enhanced reliability of higher national institute of health stroke scale thresholds over the conventional 6-point scale.

INTRODUCTION: It is still uncertain if higher thresholds on National Institute of Health Stroke Scale (NIHSS) are better predictors of large infarctions than the conventional 6-point cutoff. METHODS: We used 6-point and higher NIHSS thresholds including 8, 9, and 10-point to predict relative infarct areas, expressed as percentage of the affected hemisphere on axial brain computed tomography images, beginning at 5% with 5% increments each time until reaching the 40% cutoff for large infarctions, or achieving 100% sensitivity. Results were compared using area under the receiver operating characteristic curves (AUROC). RESULTS: We enrolled 151 patients of acute ischemic stroke (Mean age: 62.88 years ± 12.71; Female: 48.34%). 77 patients (50.99%) exhibited left hemisphere strokes, while 74 (49%) had right hemisphere involvement. Sensitivity values of the 6-point for infarcts measuring 5%, 10%, 20%, 30%, and 40% were 62%, 64%, 77%, 82%, and 100%, respectively. At 40% infarct-size, 8-point achieved comparable results (52%, 55%, 69%, 76%, 100%), closely aligning with the 9-point (50%, 53%, 69%, 76%, 100%). The10-point was slightly trailing behind in sensitivity at 40% infarct-core (96%). Moreover, higher thresholds exhibited improved false-positive rates (FPR). At 40% infarct size, the FPRs of 6, 8, 9, and 10 points were 39%, 27%, 27%, and 21% respectively. Higher thresholds had augmented AUROC values (0.86, 0.86, 0.89) as compared to the 6-point (0.80). Logistic regression identified 14-point as definitive cutoff for large infarctions. CONCLUSION: Higher thresholds can better differentiate small and medium infarcts as true-negatives and substantially reduce false-positive referrals for mechanical thrombectomy.

A comprehensive computational study to explore promising natural bioactive compounds targeting glycosyltransferase MurG in Escherichia coli for potential drug development.

Peptidoglycan is a carbohydrate with a cross-linked structure that protects the cytoplasmic membrane of bacterial cells from damage. The mechanism of peptidoglycan biosynthesis involves the main synthesizing enzyme glycosyltransferase MurG, which is known as a potential target for antibiotic therapy. Many MurG inhibitors have been recognized as MurG targets, but high toxicity and drug-resistant Escherichia coli strains remain the most important problems for further development. In addition, the discovery of selective MurG inhibitors has been limited to the synthesis of peptidoglycan-mimicking compounds. The present study employed drug discovery, such as virtual screening using molecular docking, drug likeness ADMET proprieties predictions, and molecular dynamics (MD) simulation, to identify potential natural products (NPs) for Escherichia coli. We conducted a screening of 30,926 NPs from the NPASS database. Subsequently, 20 of these compounds successfully passed the potency, pharmacokinetic, ADMET screening assays, and their validation was further confirmed through molecular docking. The best three hits and the standard were chosen for further MD simulations up to 400 ns and energy calculations to investigate the stability of the NPs-MurG complexes. The analyses of MD simulations and total binding energies suggested the higher stability of NPC272174. The potential compounds can be further explored in vivo and in vitro for promising novel antibacterial drug discovery.

Predictors of malnutrition among older residents in Qatari long-term care facilities: a retrospective study.

BACKGROUND: Malnutrition is a prevalent issue among older adults in long-term care facilities and is associated with adverse health outcomes and increased healthcare costs. Identifying the predictors of malnutrition in this population is crucial for developing effective intervention strategies. This study aimed to explore the factors contributing to malnourishment among older individuals living in long-term care facilities in Qatar. METHODS: This cross-sectional study included 75 older adults from two long-term care facilities (Rumailah Hospital and Enaya Specialized Care Center) in Qatar. Baseline characteristics, including age, sex, length of stay, mortality, weight, body mass index, co-morbidities, and laboratory parameters, were assessed. Data were analyzed using the most recent version of the SPSS software, version 29. Predictors of malnutrition and mortality were identified using logistic regression analysis. RESULTS: Of the 75 older individuals included in the study, 85% (64) were malnourished. The average age of the participants was 74.89 years, with a standard deviation of 10.21. Of all participants, approximately 61% (46) were males, and 39% (29) were females. Most malnourished older adults were classified as either at "moderate (29.69%)" or "severe risk (37.50%)," according to the Geriatric Nutritional Risk Index. Malnourished participants experienced a significant percentage of weight change within 3 months (14.01 ± 7.89); the only statistically significant predictor of malnutrition was the percentage of weight change within 3 months with an odds ratio (OR) of 4.8 (confidence interval [CI] 1.56-14.75) and p-value of 0.006. Statistically significant predictors of mortality were malnutrition (OR 24.84, CI 1.09-564) and age (OR 1.07, CI 1.00-1.14). CONCLUSIONS: A significant predictor of malnutrition in older adults identified in this study was the sudden and recent change in weight, which can be employed to detect individuals at risk early and guide tailored interventions. Malnutrition is a significant predictor of mortality. Employing a multidimensional strategy to tackle malnutrition can improve outcomes for the older individuals.

Untargeted Metabolomic Profiling Reveals Differentially Expressed Serum Metabolites and Pathways in Type 2 Diabetes Patients with and without Cognitive Decline: A Cross-Sectional Study.

Diabetes is recognized as a risk factor for cognitive decline, but the underlying mechanisms remain elusive. We aimed to identify the metabolic pathways altered in diabetes-associated cognitive decline (DACD) using untargeted metabolomics. We conducted liquid chromatography-mass spectrometry-based untargeted metabolomics to profile serum metabolite levels in 100 patients with type 2 diabetes (T2D) (54 without and 46 with DACD). Multivariate statistical tools were used to identify the differentially expressed metabolites (DEMs), and enrichment and pathways analyses were used to identify the signaling pathways associated with the DEMs. The receiver operating characteristic (ROC) analysis was employed to assess the diagnostic accuracy of a set of metabolites. We identified twenty DEMs, seven up- and thirteen downregulated in the DACD vs. DM group. Chemometric analysis revealed distinct clustering between the two groups. Metabolite set enrichment analysis found significant enrichment in various metabolite sets, including galactose metabolism, arginine and unsaturated fatty acid biosynthesis, citrate cycle, fructose and mannose, alanine, aspartate, and glutamate metabolism. Pathway analysis identified six significantly altered pathways, including arginine and unsaturated fatty acid biosynthesis, and the metabolism of the citrate cycle, alanine, aspartate, glutamate, a-linolenic acid, and glycerophospholipids. Classifier models with AUC-ROC > 90% were developed using individual metabolites or a combination of individual metabolites and metabolite ratios. Our study provides evidence of perturbations in multiple metabolic pathways in patients with DACD. The distinct DEMs identified in this study hold promise as diagnostic biomarkers for DACD patients.

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.

Profiling the autoantibody repertoire reveals autoantibodies associated with mild cognitive impairment and dementia.

Background: Dementia is a debilitating neurological disease affecting millions of people worldwide. The exact mechanisms underlying the initiation and progression of the disease remain to be fully defined. There is an increasing body of evidence for the role of immune dysregulation in the pathogenesis of dementia, where blood-borne autoimmune antibodies have been studied as potential markers associated with pathological mechanisms of dementia. Methods: This study included plasma from 50 cognitively normal individuals, 55 subjects with MCI (mild cognitive impairment), and 22 subjects with dementia. Autoantibody profiling for more than 1,600 antigens was performed using a high throughput microarray platform to identify differentially expressed autoantibodies in MCI and dementia. Results: The differential expression analysis identified 33 significantly altered autoantibodies in the plasma of patients with dementia compared to cognitively normal subjects, and 38 significantly altered autoantibodies in the plasma of patients with dementia compared to subjects with MCI. And 20 proteins had significantly altered autoantibody responses in MCI compared to cognitively normal individuals. Five autoantibodies were commonly dysregulated in both dementia and MCI, including anti-CAMK2A, CKS1B, ETS2, MAP4, and NUDT2. Plasma levels of anti-ODF3, E6, S100P, and ARHGDIG correlated negatively with the cognitive performance scores (MoCA) (r2 -0.56 to -0.42, value of p < 0.001). Additionally, several proteins targeted by autoantibodies dysregulated in dementia were significantly enriched in the neurotrophin signaling pathway, axon guidance, cholinergic synapse, long-term potentiation, apoptosis, glycolysis and gluconeogenesis. Conclusion: We have shown multiple dysregulated autoantibodies in the plasma of subjects with MCI and dementia. The corresponding proteins for these autoantibodies are involved in neurodegenerative pathways, suggesting a potential impact of autoimmunity on the etiology of dementia and the possible benefit for future therapeutic approaches. Further investigations are warranted to validate our findings.

Thiadiazine thione derivatives as anti-leishmanial agents: synthesis, biological evaluation, structure activity relationship, ADMET, molecular docking and molecular dynamics simulation studies.

During last decades, 3,5-disubstituted-tetrahydro-2H-thiadiazine-2-thione scaffold remains the center of interest due to their ease of preparation, diverse range substituents at N-3 and N-5 positions, and profound biological activities. In the current study, a series of 3,5-disubstituted-tetrahydro-2H-thiadiazine-2-thiones were synthesized in good to excellent yield, and the structure of the compounds were confirmed by various spectroscopic techniques such as FTIR, 1H-NMR, 13C-NMR and Mass spectrometry, and finally evaluated against Leishmania major. Whereas, all the evaluated compounds (1-33), demonstrate potential leishmanicidal activities with IC50 values in the range of (1.30- 149.98 uM). Among the evaluated compounds such as 3, 4, 6, and 10 exhibited excellent leishmanicidal activities with IC50 values of (2.17 µM), (2.39 µM), (2.00 µM), and (1.39 µM), respectively even better than the standard amphotericin B (IC50 = 0.50) and pentamidine (IC50 = 7.52). In order to investigate binding interaction of the most active compounds, molecular docking study was conducted with Leishmania major. Further molecular dynamic simulation study was also carried out to assess the stability and correct binding of the most active compound 10, within active site of the Leishamania major. Likewise, the physiochemical properties, drug likeness, and ADMET of the most active compounds were investigated, it was found that none of the compounds violate Lipiniski's rule of five, which show that this class of compounds had enough potential to be used as drug candidate in near future.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors.

The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R2 = 0.974, Q2 = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R2pred = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA.Communicated by Ramaswamy H. Sarma.

Increased Neck Circumference and Increased Waist-Hip Ratio: Predictive Factors of Acute Myocardial Infarction.

Background We hypothesize that neck circumference (NC) is a better predictor of acute myocardial infarction (AMI) compared to the waist-hip ratio (WHR) in patients presenting with acute coronary syndrome (ACS). The objective of this study is to investigate the association between NC and WHR with AMI and determine whether NC is a superior predictor of AMI in ACS patients compared to WHR. Methods This cross-sectional observational study was conducted in the Department of Cardiology at the Medical Teaching Institute, Lady Reading Hospital, Peshawar. The study lasted from February 20, 2018, to September 12, 2018. Patients having ACS who presented to the emergency department were enrolled via non-probability convenient sampling. Demographic data and baseline variables, including NC and WHR, were documented using a pre-designed pro forma. SPSS V.20 (IBM Corp, Armonk, NY) was used for data analysis. Continuous variables were expressed as mean ± standard deviation, while categorical variables were presented as frequencies and percentages. Chi-square tests were performed to determine the association between variables, and logistic regression models were used to measure odds ratios (ORs). Results In this study, 180 patients were included, with a mean age of 54.48±8.48 years and a male predominance of 51.5%. The results indicated a significant association between increased NC and WHR with AMI. The chi-square values for NC and WHR were 78.26 (p≤0.001) and 43.38 (p≤0.001), respectively. As NC increased from <37 cm to >38.5 cm, the OR for AMI increased from 0.46 to 4.51. Furthermore, the prevalence odds ratio (POR) of AMI increased by 2.185 times with an increase in WHR from 0.90. Conclusion Increased NC and increased WHR are statistically significantly associated and strong predictors of AMI in ACS patients. However, NC being more reliable, effective, and user-friendly should be the preferred measure.

Unilateral Variant of NGT Syndrome: An Uncommon Complication of a Very Common Intervention.

The common complications of nasogastric tube placement include gastrointestinal-tube malposition, coiling, or knotting, impaired function of the lower esophageal sphincter leading to reflux of gastric contents, esophagitis, gastrointestinal bleeding, and aspiration pneumonia. Sofferman et al. in 1990, described a clinical entity constituted by life-threatening bilateral vocal cord paralysis, presenting as throat pain, stridor and respiratory compromise as Nasogastric syndrome (NGTS). There are fewer than 50 cases of NGTS described in the literature, till date, let alone a unilateral variant of the same. Here we have described two cases of unilateral variant of NGTS, the management, outcome, and a detailed literature review of the previous reported cases. The two cases described were identified in the same year, highlighting the fact that, the entity might be more common, and needs more clinical attention than previously estimated.

Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia.

Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ -0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.

Modular and Computational Access to Innocuous Multistep Metal-Free Synthesis of 1,3,4-Oxadiazoles as Enzyme Inhibitors.

An array of 1,3,4-oxadiazole hybrids, 7a-s, structurally intriguing cores with potential in natural product synthesis and drug discovery, have been synthesized using innovative comparable conventional and microwave-assisted protocols. The synthesis was performed by the reaction of secondary amine-based acetamides, 6a-s, as the electrophile and piperidine-based oxadiazoles as the nucleophile, 3, under the metal-free reaction conditions. High yield in minimum time with highest purity was obtained by the microwave-irradiated method instead of the conventional one. The structural elucidations were made through infrared, 1H NMR, 13C NMR, and elemental analysis studies. The whole array of synthesized compounds, 7a-s, was evaluated for their potential against α-glucosidase and butyryl cholinesterase (BChE) enzymes. Natural bond orbital and structural optimizations were made by using the B3LYP method and the basis set of 6-311++G(d,p). Frontier molecular orbitals and molecular electrostatic potential were calculated at the same level of selected compounds as potential candidates against BChE and α-glucosidase enzymes utilizing the time-dependent density functional theory. Fifteen compounds out of 19 were observed to be active against α-glucosidase enzyme in comparison with acarbose as the reference standard and 7 against the BChE enzyme compared to eserine as the reference standard. The highest potential of compound 7j against BChE is well correlated by the higher binding interaction with target protein as -10.2, calculated by docking studies. The recruited compounds against both enzymes could be the best anti-enzymatic drugs and part of drugs discovery programs after further analysis.

The anti-melanogenic properties of Swietenia macrophylla king.

Fair flawless skin is the goal for some cultures and the development of irregular skin pigmentation is considered an indication of premature skin aging. Hence, there is a rising demand for skin whitening cosmetics. Thus, this research will be focusing on discovering the anti-pigmentation properties of Swietenia macrophylla seeds. Firstly, the seeds were extracted with ethanol and further fractionate based on their polarity before testing them on zebrafish embryos. The ethanolic extract of the seed demonstrated significant inhibition of both tyrosinase activity and melanin production in the embryos. However, after fractionation, the anti-melanogenic ability was observed to have decreased, signifying that the phytocompounds may be synergistic in nature. Still in the proteomic studies the ethanolic extract and its hexane fraction both induced the downregulation of cathepsin LB and cytoskeletal proteins that have connections to the melanogenic pathway, confirming that S. macrophylla seeds do indeed have anti-pigmentation properties that can be exploited for cosmetic use. Next, limonoids (tetranortriterpenoids found in the seed) were tested for their inhibitory effect against human tyrosinase related protein 1 (TYRP-1) via molecular docking. It was found that limonoids have a stronger binding affinity to TYRP-1 than kojic acid, suggesting that these phytocompounds may have the potential in inhibiting pigmentation. However, this still needs further confirmation before these phytocompounds can be developed into a skin whitening agent. Other assays like ex-vivo or 3D human skin culture can also be used to better study the seeds anti-pigmentation effect on humans.

Solitary Rectal Ulcer Syndrome in Patients Presenting With Lower Gastrointestinal Bleeding: A Tertiary-Care Hospital Experience.

BACKGROUND: Solitary rectal ulcer syndrome (SRUS) is a benign rectal condition associated with defecation disorder that has multifactor pathologies and variable findings on presentation, endoscopy, and histopathology. A diagnostic dilemma with an overlap of differentials and step-wise management that starts with conservative therapies and goes up to repeated surgeries in case of failure of the conservative approach. OBJECTIVE: This study aims to observe clinical, endoscopic, and histological features of SRUS in patients presenting with lower gastrointestinal bleeding. MATERIAL AND METHODS: The study was conducted at the Department of Gastroenterology, Medical Teaching Institute, Lady Reading Hospital Peshawar from October 2018 to April 2020. After written informed consent, 257 patients (149 males and 108 females) from ages 15 to 70 who presented with lower GI bleeding were included via non-probability convenient sampling. Sociodemographic details were recorded in a pre-designed proforma. A colonoscopy was performed with the Colonoscope CF200 Z, Olympus Tokyo, Japan, and findings were noted. Suspected lesions were magnified, dyed with 0.2% indigo carmine, biopsied from the middle and edges of the ulcer, and sent for histopathology. All data were recorded and analyzed in SPSS-20. The mean with SD was calculated for quantitative variables, and frequency and percentages were calculated for qualitative variables. The chi-square test was used to check the significance, and a p-value of <0.05 was considered statistically significant. RESULTS: SRUS was found in 17 (6.6%) patients with lower GI bleeding, with a male predominance of 57% (n=11). Perirectal bleeding, constipation, mucous discharge, abdominal pain, and anemia were common clinical findings. Solitary lesions, ulceration, and anterior rectum location were the most common endoscopy findings. Obliterated lamina propria with collagen, ulceration, crypt distortion, and inflammatory infiltrates were common histopathological findings. CONCLUSION: SRUS is a benign defecation disorder commonly presenting with lower GI bleeding, constipation, straining, and abdominal pain. It needs a stepwise approach with conservative management, medical management, biofeedback, and surgeries as a last resort.

Corneal nerve loss predicts dementia in patients with mild cognitive impairment.

OBJECTIVES: This study compared the utility of corneal nerve measures with brain volumetry for predicting progression to dementia in individuals with mild cognitive impairment (MCI). METHODS: Participants with no cognitive impairment (NCI) and MCI underwent assessment of cognitive function, brain volumetry of thirteen brain structures, including the hippocampus and corneal confocal microscopy (CCM). Participants with MCI were followed up in the clinic to identify progression to dementia. RESULTS: Of 107 participants with MCI aged 68.4 ± 7.7 years, 33 (30.8%) progressed to dementia over 2.6-years of follow-up. Compared to participants with NCI (n = 12), participants who remained with MCI (n = 74) or progressed to dementia had lower corneal nerve measures (p < 0.0001). Progressors had lower corneal nerve measures, hippocampal, and whole brain volume (all p < 0.0001). However, CCM had a higher prognostic accuracy (72%-75% vs 68%-69%) for identifying individuals who progressed to dementia compared to hippocampus and whole brain volume. The adjusted odds ratio for progression to dementia was 6.1 (95% CI: 1.6-23.8) and 4.1 (95% CI: 1.2-14.2) higher with abnormal CCM measures, but was not significant for abnormal brain volume. INTERPRETATION: Abnormal CCM measures have a higher prognostic accuracy than brain volumetry for predicting progression from MCI to dementia. Further work is required to validate the predictive ability of CCM compared to other established biomarkers of dementia.

Virtual screening of bioactive anti-SARS-CoV natural products and identification of 3ß,12-diacetoxyabieta-6,8,11,13-tetraene as a potential inhibitor of SARS-CoV-2 virus and its infection related pathways by MD simulation and network pharmacology.

Since the first prevalence of COVID-19 in 2019, it still remains the most devastating pandemic throughout the world. The current research aimed to find potential natural products to inhibit the novel coronavirus and associated infection by MD simulation and network pharmacology approach. Molecular docking was performed for 39 natural products having potent anti-SARS-CoV activity. Five natural products showed high binding interaction with the viral main protease for the SARS-CoV-2 virus, where 3ß,12-diacetoxyabieta-6,8,11,13 tetraene showed stable binding in MD simulation until 100 ns. Both 3ß,12-diacetoxyabieta-6,8,11,13 tetraene and tomentin A targeted 11 common genes that are related to COVID-19 and interact with each other. Gene ontology development analysis further showed that all these 11 genes are attached to various biological processes. The KEGG pathway analysis also showed that the proteins that are targeted by 3ß,12-diacetoxyabieta-6,8,11,13 tetraene and tomentin A are associated with multiple pathways related to COVID-19 infection. Furthermore, the ADMET and MDS studies reveals 3ß,12-diacetoxyabieta-6,8,11,13 as the best-suited compound for oral drug delivery.Communicated by Ramaswamy H. Sarma.

Identification of dual active sites in Caenorhabditis elegans GANA-1 protein: an ortholog of the human α-GAL a and α-NAGA enzymes.

Fabry disease (FD) is caused by a defective α-galactosidase A (α-GAL A) enzyme responsible for breaking down globotriaosylceramide (Gb3). To develop affordable therapeutics, more effort is needed to obtain insights into the underlying mechanism of FD and understanding human α-GAL A structure and function in related animal models. We adopted C. elegans as a model to elucidate the sequence and 3D structure of its GANA-1 enzyme and compared it to human α-GAL A. We constructed GANA-1 3D structure by homology modelling and validated the quality of the predicted GANA-1 structure, followed by computational docking of human ligands. The GANA-1 protein shared sequence similarities up to 42.1% with the human α-GAL A in silico and had dual active sites. GANA-1 homology modelling showed that 11 out of 13 amino acids in the first active site of GANA-1 protein overlapped with the human α-GAL A active site, indicating the prospect for substrate cross-reaction. Computational molecular docking using human ligands like Gb3 (first pocket), 4-nitrophenyl-α-D-galactopyranoside (second pocket), α-galactose (second pocket), and N-acetyl-D-galactosamine (second pocket) showed negative binding energy. This revealed that the ligands were able to bind within both GANA-1 active sites, mimicking the human α-GAL A and α-NAGA enzymes. We identified human compounds with adequate docking scores, predicting robust interactions with the GANA-1 active site. Our data suggested that the C. elegans GANA-1 enzyme may possess structural and functional similarities to human α-GAL A, including an intrinsic capability to metabolize Gb3 deposits.Communicated by Ramaswamy H. Sarma.