A Comparative Study of Elective Sublay Versus Onlay Repair for Non-complex, Small, and Medium-Size Incisional Hernia: Post-operative Complications in a Tertiary Hospital in Ranchi, India.

BACKGROUND: The most difficult hernia surgery is the repair of the ventral hernia, which is caused by aberrant organ or tissue protrusions through the abdominal wall. Factors like obesity, smoking, and chronic medical conditions contribute to their formation. Surgical strategies have evolved from anatomical repair to mesh hernioplasty, with mesh placement playing a significant role in outcomes. The ideal anatomical location for mesh placement remains debated due to varying results. So, the objective of the study is to compare early postoperative complications, surgical site infection, and incidence of recurrence between sublay and onlay mesh placement repair of incisional hernias of <10 cm in diameter, at a tertiary hospital in Ranchi. METHODS:  This retrospective comparative study was conducted over a period of January 2022 to January 2024 at the Rajendra Institute of Medical Science, Ranchi, India. During the study period, 96 patients were operated on, and their demographic details, along with their position of mesh placement and postoperative complications (seroma formation, wound infection, postoperative hospital stays, and recurrence), were retrieved from the hospital data. Comparisons between onlay and sublay groups in terms of post-operative complications were made. RESULTS: Within the study period, a total of 96 patients were operated on for incisional hernia. In this study, 36 (37.5%) were male and 60 (62.5%) were female, with a male-to-female ratio of 0.6:1. Out of the total number of patients, 56 (58.4%) had a past history of emergency surgery. It was observed that there was a higher incidence of seroma formation in the onlay group compared to the sublay with a statistical significance p-value of 0.027. The incidence of wound infection was found to be statistically significant (p-value = 0.035) between the onlay and sublay groups. In a period of six-month follow-up, three patients of the total study population had an incidence of recurrent incisional hernia, of which two from the onlay group and one from the sublay group were present, and there was no statistical significance (p-value > 0.5). CONCLUSIONS: Based on our retrospective analysis, we can say that there is a lower incidence of postoperative complications and recurrence in sublay repair, along with a shorter postoperative hospital stay, making it a preferred method of repair over onlay.

Effectiveness of Mannheim's Peritonitis Index in Patients With Peritonitis Secondary to Hollow Viscus Perforation in a Tertiary Care Hospital in Jharkhand, India.

Introduction Peritonitis refers to the inflammation of the peritoneum and peritoneal cavity. Causes of peritonitis can be bacterial (gastrointestinal or non-gastrointestinal), chemical, traumatic, or ischemic. Peritonitis can be localized or diffuse, acute or chronic. Peritonitis can be primary, secondary, or tertiary, according to the pathogenesis. Peritonitis developed secondary to hollow viscus perforation is a life-threatening condition and a common cause of emergency surgery in India. The Mannheim peritonitis index (MPI) is a simple scoring system that can accurately predict the outcome of peritonitis. This study aimed to evaluate the effectiveness of MPI in predicting mortality risk or prognosis in patients with peritonitis due to hollow viscus perforation. Materials and methods This observational cross-sectional study at the Department of General Surgery, Rajendra Institute of Medical Sciences, Ranchi, involved 111 patients with peritonitis due to hollow viscus perforation from December 2021 to March 2022. Detailed history, clinical examination, relevant blood tests, and radiological investigations established a diagnosis of perforation peritonitis, followed by a score assessment. Data were analyzed using SPSS software (IBM Corp., Armonk, NY, USA). Results Patients >50 years had higher mortality (i.e., 18/43) than patients <50 years (i.e., 13/68). Overall mortality was 31, which included one in low risk, 12 in intermediate risk, and 18 in the high-risk group. Mortality was lowest in the low-risk group (i.e., 1/30), highest in the high-risk group (i.e., 18/40), and 12/41 in the intermediate-risk group; the p-value was <0.05, which was highly significant. Mortality was higher in patients presenting after 24 hours, having organ failure, and non-colonic sepsis. Conclusion The MPI scoring system is simple, easy to calculate, cost-effective, precise, and effective in assessing mortality and morbidity risk in patients with peritonitis due to hollow viscus perforation. It can also guide further management strategies.

A Descriptive Study of the Demographic and Clinicopathological Characteristics of Individuals With Gallstone-Induced Pancreatitis in a Tertiary-Level Hospital in Ranchi, Jharkhand.

BACKGROUND: Gallstones are a major cause of acute pancreatitis, which is associated with high recurrence, morbidity, and mortality. Careful consideration of demographic and clinicopathological features is required to understand the association between the cause and severity of pancreatitis in various populations, and such crucial information is lacking for Jharkhand's population. Here, we sought to describe the demographic and clinicopathological features of gallstone-induced acute pancreatitis at a tertiary hospital in Ranchi. METHODS: This hospital-based descriptive study was conducted at Rajendra Institute of Medical Sciences in Ranchi. The hospital records of patients admitted to the surgical unit with acute gallstone-induced pancreatitis from January 2023 to December 2023 were analyzed. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. RESULTS: Of the 72 patients admitted with acute gallstone-induced pancreatitis (mean age: 42.5 years), 46 (64%) were males and 26 (36%) were females. All 72 patients had abdominal pain and 44 (61%) were vomiting. The severe vs. non-severe pancreatitis groups differed significantly in age (≥40) and male gender (p = 0.013 and 0.031, respectively). A total of 45 (62.5%) patients had severe gallstone-induced pancreatitis, and the most common complication was acute kidney injury, followed by pleural effusion (18 (25%) and 13 (18.1%) cases, respectively). CONCLUSIONS: Our study revealed that gallstone-induced pancreatitis was more common in males and that age and gender were significantly associated with severity. However, late presentation to the hospital may have influenced our study, resulting in more severe cases being reported, with the most common complication being acute kidney injury. To our knowledge, this is the first study to describe the demographic, clinicopathological, and outcome data of acute gallstone-induced pancreatitis in Ranchi. These results can guide hospital policy development to improve patient outcomes.

Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system.

Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar's anthesis and physiological maturity, with observed value falling within 5% of the model's predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9-16%. The study also demonstrated the model's ability to accurately capture soil nitrate-N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop's growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.

A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

The rigidity and chelation effect of ligands on the hydrogen evolution reaction catalyzed by Ni(II) complexes.

With increasing interest in nickel-based electrocatalysts, three heteroleptic Ni(II) dithiolate complexes with the general formula [Ni(II)L(L')2] (1-3), L = 2-(methylene-1,1'-dithiolato)-5,5'-dimethylcyclohexane-1,3-dione and L' = triphenylphosphine (1), 1,1'-bis(diphenylphosphino)ferrocene (DPPF) (2), and 1,2-bis(diphenylphosphino)ethane (DPPE) (3), have been synthesized and characterized by various spectroscopic techniques (UV-vis, IR, 1H, and 31P{1H} NMR) as well as the electrochemical method. The molecular structure of complex 2 has also been determined by single-crystal X-ray crystallography. The crystal structure of complex 2 reveals a distorted square planar geometry around the nickel metal ion with a NiP2S2 core. The cyclic voltammograms reveal a small difference in the redox properties of complexes (ΔE° = 130 mV) while the difference in the catalytic half-wave potential becomes substantial (ΔEcat/2 = 670 mV) in the presence of 15 mM CF3COOH. The common S^S-dithiolate ligand provides stability, while the rigidity effect of other ligands (DPPE (3) > DPPF (2) > PPh3 (1)) regulates the formation of the transition state, resulting in the NiIII-H intermediate in the order of 1 > 2 > 3. The foot-of-the-wave analysis supports the widely accepted ECEC mechanism for Ni-based complexes with the first protonation step as a rate-determining step. The electrocatalytic proton reduction activity follows in the order of complex 1 > 2 > 3. The comparatively lower overpotential and higher turnover frequency of complex 1 are attributed to the flexibility of the PPh3 ligand, which favours the easy formation of a transition state.

Investigate the efficacy of size exclusion chromatography for the isolation of extracellular vesicles from C. elegans.

Isolation of Extracellular Vesicles (EVs) has been done extensively in the past using ultracentrifugation, a recent shift has been observed towards precipitation, and exosome isolation kits. These methods often co-elute contaminants of similar size and density which makes their detection and downstream applications quite challenging. As well as the EV yield is also compromised in some methodologies due to aggregate formation. In recent reports, size-exclusion chromatography (SEC) is replacing density gradient-based ultracentrifugation as the gold standard of exosome isolation. It outperforms in yield, purity and does not account for any physical damage to the EVs. We have standardized the methodology for an efficient pure yield of homogenous exosomes of size even smaller than 75 nm in Caenorhabditis elegans homogenate. The paper entails the application and optimization of EV isolation by SEC based on previous studies by optimizing bed size and type of sepharose column employed. We propose that this method is economically feasible in comparison with currently available approaches. A comparative study was conducted to investigate the performance of CL-6B in relation to CL-2B and further, this was combined with ultracentrifugation for higher efficacy. The methodology could be introduced in a clinical setting due to its therapeutic potential and scope. The eluted EVs were studied by flow cytometry, nanotracking and characterized for size and morphology.

A systematic review on automatic identification of insomnia.

Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual's sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection. Methods: We followed the recommendations given in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a se- lection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various meth- ods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intel- ligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this de- tection task. Results: Result: The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential com- ponents under investigation in the automated technique include the data input source, objective, machine learning (ML) and deep learning (DL) network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types. Conclusion: Based on our review of the studies featured in this paper, we have identi- fied a notable research gap in the current methods for identifying insomnia and oppor- tunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia. .

Diagnostic Role of Transvaginal Sonography and Magnetic Resonance Imaging in Adenomyosis of the Uterus and its Correlation with Histopathology.

Background and Objective: The prevalence of adenomyosis of the uterus varies from 5% to 70%, and there is no clear consensus on its imaging diagnostic criteria. The objective of this study was to evaluate the role of transvaginal sonography (TVS), combined TVS and color Doppler (TVS-CD), and magnetic resonance imaging (MRI) in the diagnosis of adenomyosis. Materials and Methods: This was a tertiary care hospital-based prospective study, in which 365 clinically suspected cases of adenomyosis were enrolled. All three types of imaging (TVS, TVS-CD, and MRI) were done in 233/365 patients, followed by hysterectomy in 50. Imaging features were correlated with the histopathological examination (HPE), which was taken as the gold standard for the diagnosis. The diagnostic performance of each imaging modality was assessed. Results: Among patients who underwent hysterectomy, 36/50 (72%) had adenomyosis on HPE, with or without associated benign gynecological abnormalities. Sensitivity, specificity, positive predictive value (PPV), negative PV (NPV), and diagnostic accuracy (DA) of MRI were higher than that of TVS-CD (91.67% vs. 77.78%, 85.71% vs. 78.57%, 94.29% vs. 90.32%, 80% vs. 57.89%, and 90% vs. 78%, respectively). TVS alone had lower diagnostic performance (specificity: 64.29%, PPV 84.85%, NPV 52.94%, and DA74%) than TVS-CD, but equal sensitivity (77.78%). Heterogeneous myometrium was the most sensitive (80.56%), while myometrial cyst was the most specific (92.86%) TVS feature. The maximum junctional zone thickness ≥12 mm was the most sensitive (97.22%), while the hyperintense myometrial focus was the most specific (100%) MRI feature. Conclusion: TVS-CD should be used as an initial diagnostic imaging modality in clinically suspected cases of adenomyosis; however, MRI due to better diagnostic efficacy should be the imaging modality of choice before subjecting such patients to hysterectomy.

INSOMNet: Automated insomnia detection using scalogram and deep neural networks with ECG signals.

Sleep is a natural state of rest for the body and mind. It is essential for a human's physical and mental health because it helps the body restore itself. Insomnia is a sleep disorder that causes difficulty falling asleep or staying asleep and can lead to several health problems. Conventional sleep monitoring and insomnia detection systems are expensive, laborious, and time-consuming. This is the first study that integrates an electrocardiogram (ECG) scalogram with a convolutional neural network (CNN) to develop a model for the accurate measurement of the quality of sleep in identifying insomnia. Continuous wavelet transform has been employed to convert 1-D time-domain ECG signals into 2-D scalograms. Obtained scalograms are fed to AlexNet, MobileNetV2, VGG16, and newly developed CNN for automated detection of insomnia. The proposed INSOMNet system is validated on the cyclic alternating pattern (CAP) and sleep disorder research center (SDRC) datasets. Six performance measures, accuracy (ACC), false omission rate (FOR), sensitivity (SEN), false discovery rate (FDR), specificity (SPE), and threat score (TS), have been calculated to evaluate the developed model. Our developed system attained the classifications ACC of 98.91%, 98.68%, FOR of 1.5, 0.66, SEN of 98.94%, 99.31%, FDR of 0.80, 2.00, SPE of 98.87%, 98.08%, and TS 0.98, 0.97 on CAP and SDRC datasets, respectively. The developed model is less complex and more accurate than transfer-learning networks. The prototype is ready to be tested with a huge dataset from diverse centers.

Association of d-dimer levels with in-hospital outcomes among COVID-19 positive patients: a developing country multicenter retrospective cohort.

D-dimer levels, which originate from the lysis of cross-linked fibrin, are serially measured during coronavirus disease 2019 illness to rule out hypercoagulability as well as a septic marker. Methods: This multicenter retrospective study was carried out in two tertiary care hospitals in Karachi, Pakistan. The study included adult patients admitted with a laboratory-confirmed coronavirus disease 2019 infection, with at least one measured d-dimer within 24 h following admission. Discharged patients were compared with the mortality group for survival analysis. Results: The study population of 813 patients had 68.5% males, with a median age of 57.0 years and 14.0 days of illness. The largest d-dimer elevation was between 0.51-2.00 mcg/ml (tertile 2) observed in 332 patients (40.8%), followed by 236 patients (29.2%) having values greater than 5.00 mcg/ml (tertile 4). Within 45 days of hospital stay, 230 patients (28.3%) died, with the majority in the ICU (53.9%). On multivariable logistic regression between d-dimer and mortality, the unadjusted (Model 1) had a higher d-dimer category (tertile 3 and tertile 4) associated with a higher risk of death (OR: 2.15; 95% CI: 1.02-4.54, P=0.044) and (OR: 4.74; 95% CI: 2.38-9.46, P<0.001). Adjustment for age, sex, and BMI (Model 2) yields only tertile 4 being significant (OR: 4.27; 95% CI: 2.06-8.86, P<0.001). Conclusion: Higher d-dimer levels were independently associated with a high risk of mortality. The added value of d-dimer in risk stratifying patients for mortality was not affected by invasive ventilation, ICU stays, length of hospital stays, or comorbidities.

Comparative study of wound infection between elective and emergency abdominal surgeries: a retrospective cohort study.

The objective of this study was to determine the frequency of wound infection among patients with abdominal surgeries and to compare the surgical site infection following elective versus emergency abdominal surgeries in a tertiary care hospital. Subjects and methods: All patients who fulfilled the inclusion criteria in the Department of General Surgery were included in the study. After taking informed written consent history was taken, clinical examination and patients were divided into two groups: group A (elective abdominal surgery) and group B (emergency abdominal surgery), patients in both groups were compared for the outcome that is surgical site infection. Result: A total of 140 patients who underwent abdominal surgery were included. Wound infection in abdominal surgeries was noted in 26 patients (18.6%), in group A wound infection was noted in 7 (5%), while in group B wound infection was seen in 19 (13.6%). Conclusion: The rate of wound infection in patients with abdominal surgeries was not low among the study population and the rate of wound infection was higher in emergency abdominal surgeries as compared with elective abdominal surgeries.

Bilayer fixed-dose combination tablet for curcumin microparticles and piroxicam and in vitro evaluation.

Aim: In the present work, fixed-dose combination of bilayer tablets for piroxicam as and curcumin as immediate-release and sustained-release layer (SRL) respectively for management of inflammatory response. Materials & methods: The SRL include Curcumin polycaprolactone microparticles from spray drying. The tablet layers include Pearlitol 200SD, Microcrystalline cellulose PH101, Aerosil 200, talc each layer. Results: SEM studies confirm spherical microparticles. PXRD and DSC studies confirm the amorphous microparticles. In vitro studies exhibit, an immediate release and sustained release for Piroxicam and Curcumin after 2 h. Cellular uptake studies on RAW 264.7 cells confirm the complete internalization of microparticles. Conclusion: Therefore, it was concluded that microparticles can be formulated into a unit dosage form for the management of inflammation.

Chlorocobaloxime containing N-(4-pyridylmethyl)-1,8-naphthalamide peripheral ligands: synthesis, characterization and enhanced electrochemical hydrogen evolution in alkaline medium.

Two new discrete cobaloxime based complexes with the general formula [ClCo(dioxime)2L] (1 and 2), L1 = N-(4-pyridylmethyl)-1,8-naphthalamide, L2 = 4-bromo-N-(4-pyridylmethyl)-1,8-naphthalamide have been synthesized and characterized by various spectroscopic techniques such as FT-IR, 1H, 13C{1H} NMR and PXRD. The molecular structures of both complexes have also been determined using single crystal X-ray crystallography. The solid state molecular structures revealed distorted octahedral geometry around the Co(III) central metal ion with two dioximes in the equatorial plane and axial positions are occupied by chloro and pyridine nitrogen of N-(4-pyridylmethyl)-1,8-naphthalamide ligands. Both complexes exhibit weaker non-covalent interactions (C-H⋯O, C-H⋯Cl and C-H⋯π(Centroid) in complex 1 whereas C-H⋯O and C-H⋯Br in complex 2) resulting in the formation of dimeric and 1D supramolecular structures. Furthermore, these complexes are immobilized onto the surface of activated carbon cloth (CC) and their electrocatalytic performance for the hydrogen evolution reaction (HER) has been investigated in alkaline and acidic media as well as buffer solution. In alkaline medium, we found that complex 2 exhibited impressive electrocatalytic HER activity and produced a current density of -10 mA cm-2 at an overpotential of 260 mV, whereas complex 1 produced the same current density at an overpotential of 334 mV. An electrochemical impedance spectroscopy (EIS) spectral study revealed the faster charge transfer kinetics of complex 2 than that of complex 1. Similarly, the low Tafel slope (100 mV dec-1) for the HER with complex 2 indicates faster HER kinetics compared to complex 1. The chronoamperometric study showed that complex 2 is stable under electrocatalytic HER conditions for 5 h without losing the initial current density and it has also been established that the complex structure is retained after electrocatalysis.

Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends.

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

Optimizing Skin Cancer Survival Prediction with Ensemble Techniques.

The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan-Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy.

Omicron BA.2 lineage predominance in severe acute respiratory syndrome coronavirus 2 positive cases during the third wave in North India.

Background: Recent studies on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reveal that Omicron variant BA.1 and sub-lineages have revived the concern over resistance to antiviral drugs and vaccine-induced immunity. The present study aims to analyze the clinical profile and genome characterization of the SARS-CoV-2 variant in eastern Uttar Pradesh (UP), North India. Methods: Whole-genome sequencing (WGS) was conducted for 146 SARS-CoV-2 samples obtained from individuals who tested coronavirus disease 2019 (COVID-19) positive between the period of 1 January 2022 and 24 February 2022, from three districts of eastern UP. The details regarding clinical and hospitalized status were captured through telephonic interviews after obtaining verbal informed consent. A maximum-likelihood phylogenetic tree was created for evolutionary analysis using MEGA7. Results: The mean age of study participants was 33.9 ± 13.1 years, with 73.5% accounting for male patients. Of the 98 cases contacted by telephone, 30 (30.6%) had a travel history (domestic/international), 16 (16.3%) reported having been infected with COVID-19 in past, 79 (80.6%) had symptoms, and seven had at least one comorbidity. Most of the sequences belonged to the Omicron variant, with BA.1 (6.2%), BA.1.1 (2.7%), BA.1.1.1 (0.7%), BA.1.1.7 (5.5%), BA.1.17.2 (0.7%), BA.1.18 (0.7%), BA.2 (30.8%), BA.2.10 (50.7%), BA.2.12 (0.7%), and B.1.617.2 (1.3%) lineages. BA.1 and BA.1.1 strains possess signature spike mutations S:A67V, S:T95I, S:R346K, S:S371L, S:G446S, S:G496S, S:T547K, S:N856K, and S:L981F, and BA.2 contains S:V213G, S:T376A, and S:D405N. Notably, ins214EPE (S1- N-Terminal domain) mutation was found in a significant number of Omicron BA.1 and sub-lineages. The overall Omicron BA.2 lineage was observed in 79.5% of women and 83.2% of men. Conclusion: The current study showed a predominance of the Omicron BA.2 variant outcompeting the BA.1 over a period in eastern UP. Most of the cases had a breakthrough infection following the recommended two doses of vaccine with four in five cases being symptomatic. There is a need to further explore the immune evasion properties of the Omicron variant.

Pulse oximetrySpO2signal for automated identification of sleep apnea: a review and future trends.

Sleep apnea (SA) is characterized by intermittent episodes of apnea or hypopnea paused or reduced breathing, respectively each lasting at least ten seconds that occur during sleep. SA has an estimated global prevalence of 200 million and is associated with medical comorbidity, and sufferers are also more likely to sustain traffic- and work-related injury due to daytime somnolence. SA is amenable to treatment if detected early. Polysomnography (PSG) involving multi-channel signal acquisition is the reference standard for diagnosing SA but is onerous and costly. For home-based detection of SA, single-channelSpO2signal acquisition using portable pulse oximeters is feasible. Machine (ML) and deep learning (DL) models have been developed for automated classification of SA versus no SA usingSpO2signals alone. In this work, we review studies published between 2012 and 2022 on the use of ML and DL forSpO2signal-based diagnosis of SA. A literature search based on PRISMA recommendations yielded 297 publications, of which 31 were selected after considering the inclusion and exclusion criteria. There were 20 ML and 11 DL models; their methods, differences, results, merits, and limitations were discussed. Many studies reported encouraging performance, which indicates the utility ofSpO2signals in wearable devices for home-based SA detection.

Microbes-mediated integrated nutrient management for improved rhizo-modulation, pigeonpea productivity, and soil bio-fertility in a semi-arid agro-ecology.

Excessive dependence on chemical fertilizers and ignorance to organic and microbial inputs under intensive cropping systems are the basic components of contemporary agriculture, which evolves several sustainability issues, such as degraded soil health and sub-optimal crop productivity. This scenario urges for integrated nutrient management approaches, such as microbes-mediated integrated plant nutrition for curtailing the high doses as chemical fertilizers. Rationally, experiment has been conducted in pigeonpea at ICAR-IARI, New Delhi, with the aim of identifying the appropriate nutrient management technique involving microbial and organic nutrient sources for improved rhizo-modulation, crop productivity, and soil bio-fertility. The randomized block-designed experiment consisted nine treatments viz. Control, Recommended dose of fertilizers (RDF), RDF+ Microbial inoculants (MI), Vermicompost (VC), Farm Yard Manure (FYM), Leaf Compost (LC), VC + MI, FYM + MI, and LC + MI. Rhizobium spp., Pseudomonas spp., Bacillus spp., and Frateuria aurantia were used as seed-inoculating microbes. The results indicated the significant response of integration following the trend VC + MI > FYM + MI > LC + MI > RDF + MI for various plant shoot-root growth attributes and soil microbial and enzymatic properties. FYM + MI significantly improved the water-stable aggregates (22%), mean weight diameter (1.13 mm), and geometric mean diameter (0.93 mm), soil organic carbon (SOC), SOC stock, and SOC sequestration. The chemical properties viz. available N, P, and K were significantly improved with VC + MI. The study summarizes that FYM + MI could result in better soil physico-chemical and biological properties and shoot-root development; however; VC + MI could improve available nutrients in the soil and may enhance the growth of pigeonpea more effectively. The outcomes of the study are postulated as a viable and alternative solution for excessive chemical fertilizer-based nutrient management and would also promote the microbial consortia and organic manures-based agro-industries. This would add to the goal of sustainable agricultural development by producing quality crop produce, maintaining agro-biodiversity and making the soils fertile and healthy that would be a "gift to the society."

Sural Sensory Nerve Action Potential: A Study in Healthy Indian Subjects at Tertiary Care Center of North-West India.

Objectives: This study was done to obtain the reference data for the sural SNAP amplitude and latency at distances of 14, 12, and 10 cm from the active recording electrode in Indian healthy subjects for different age groups. Material and Methods: Two hundred forty-four healthy subjects (18-80 years) were included in this cross-sectional study. Subjects were divided into six groups according to age. Sural SNAP was recorded antidromically stimulating at three sites (14, 12, and 10 cm from the recording electrode). The quantitative variables were expressed as Mean ± SD/Median (IQR) and compared using t test/ANOVA. Transformed data for amplitude were analyzed with the use of paired t test. P < 0.05 was considered statistically significant. SPSS version 20.0 software was used for statistical analysis. Results: Mean age of included subjects was 43.28 years. Maximum leg girth was at 14 cm. Analysis showed a significant difference in the leg girth at all three sites (P < 0.001). Sural SNAP latency at each stimulating site was compared in different age groups, no significant difference was found between groups (P = 0.19). Maximum amplitude was in the 18-30-years age group and amplitude was minimum in the 71-80-years age group (4.34 and 2.79, respectively). The difference in the amplitude recorded in the different age groups was found to be statistically different (P < 0.001). The difference in the amplitude recorded at each site was found to be statistically different (P < 0.001). Conclusion: This is the first study with a large sample size (244 subjects) to provide age-stratified reference data for SNAP in the Indian population by using three sites of stimulation at distances of 14, 12, and 10 cm from the recording electrode. This study shows that sural SNAP amplitude varies with the age of the subject and distance from stimulation.