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BackgroundAlzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM).MethodsSGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years).ResultsPatients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition.ConclusionsThese results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Heart disease or Cardiovascular illness is the most prevalent cause of mortality globally. The challenge of predicting heart illness using clinical data analytics is considerable. Machine learning (ML) has been extensively used in the medical domain for disease prediction. This work performs a comparative analysis of various oversampling methods like the Synthetic Minority Oversampling technique (SMOTE), Synthetic Minority Oversampling Technique with Edited Nearest Neighbor (SMOTE-ENN), and Adaptive Synthetic Sampling Approach (ADASYN) Algorithm used with ML classifiers on imbalanced heart failure prediction dataset. Six ML classifiers are analyzed in the study Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). The accuracy metric is used to measure the model’s performance The result depicts that the ADASYN technique performs better for the given dataset and increases the accuracy of the classifier in heart failure prediction.

Introduction: Breast pathology encompasses a wide spectrum, ranging from benign conditions to invasive cancers, posing significant diagnostic challenges. Most breast lesions are usually divided into benign and malignant disorders, and their prognosis can often be reasonably predicted. However, certain tumours exhibit borderline characteristics and occupy a grey area between benign and malignant, making it difficult to forecast their course with any degree of accuracy. Aim: To assess the histopathological spectrum of benign and borderline breast lesions. Materials and Methods: This cross-sectional study, conducted over a period of five years (January 2019 to December 2023) at Shyam Shah Medical College, Rewa, Madhya Pradesh, India analysed 831 cases of breast lesions to determine their clinicopathological features, such as laterality of the breast and gross features. Grossing of the specimens was performed, and details were noted. Tissue pieces were processed, and sections were stained using conventional Haematoxylin and Eosin (H&E) staining. Results: Out of 831 breast lesions, 640 (77.0%) cases were benign, 15 (1.8%) were borderline, and 176 (21.2%) were malignant. Fibroadenoma was the most frequent lesion in females with 419 cases (50.4%), with a mean age of 21.4 years. In males, gynaecomastia was the most common finding in 21 (2.5%), with a mean age of 17 years. The second most common lesion overall was fibrocystic disease 148 (17.8%), with a mean age of 33.5 years. Other benign lesions included inflammatory breast diseases 21 (2.5%), lactational adenoma 2 (0.24%), sclerosing adenosis 7 (0.84%), microglandular adenosis 8 (0.96%), tubular adenoma 7 (0.84%), benign phyllodes 4 (0.48%), and lipoma and hamartoma 3 (0.36% combined). Borderline lesions included atypical ductal hyperplasia 9 (1.08%), ductal carcinoma in-situ 3 (0.36%), and borderline phyllodes tumours 2 (0.12%). Conclusion: In the study population, the range of benign breast diseases appears consistent with findings from other studies, with fibroadenoma emerging as the predominant benign lesion, followed by fibrocystic changes. While premalignant lesions were less prevalent in present study, a thorough evaluation of all cases of breast lesions is recommended to rule out the potential for breast cancer.

The widely advertised category of unhealthy food and beverages (F&B) is calorie-dense, nutrient-poor, and associated with long-term harm, particularly for children. Business literature has been largely silent regarding the ethicality of its advertising targeting children and lacks clarity on questions future research needs to address. Based on a bibliometric analysis of 1,851 articles published between 1973 and 2021 containing 70,379 citations, we uncovered five intellectual clusters underlying research on the ethics of unhealthy F&B advertising to children (ATC). Importantly, we propose a concrete roadmap featuring actionable and pertinent unanswered questions to stimulate research in this crucial domain.

Abstract A highly efficient approach for synthesizing a supramolecular metallogel of Co(II) ions, denoted as CoA-TA, has been established under room temperature and atmospheric pressure conditions. This method employs the metal-coordinating organic ligand benzene-1,3,5-tricarboxylic acid as a low molecular weight gelator (LMWG) in DMF solvent. A comprehensive analysis of the mechanical properties of the resulting supramolecular Co(II)-metallogel was conducted through rheological investigation, considering angular frequency and thixotropic study. The hierarchical rocky network structure of the supramolecular Co(II)-metallogel was unveiled using field emission scanning electron microscopy (FESEM). Transmission electron microscopic (TEM) analysis showed rod-shaped structures via low-magnification high angle annular dark field (HAADF) bright field scanning transmission electron microscopic (STEM) imaging, while energy dispersive X-ray (EDX) elemental mapping confirmed its primary chemical constituents. The formation mechanism of the metallogel was examined via fourier transform infrared spectroscopy (FTIR) spectroscopy. The nature of the synthesized CoA-TA metallogel was affirmed through powder X-ray diffraction (PXRD) analysis. Furthermore, this study involved fabrication of Schottky diode structures in a metal-semiconductor-metal geometry based on cobalt(II) metallogel (CoA-TA), enabling observation of charge transport behavior. Remarkably, a resistive random access memory (RRAM) device utilizing cobalt(II) metallohydrogel (CoA-TA) demonstrated bipolar resistive switching at room temperature and under ambient conditions. The switching mechanism was investigated, revealing the formation and rupture of conductive filaments between metal electrodes that govern the resistive switching behavior. This RRAM device exhibited an impressive ON/OFF ratio (~ 414) and exceptional endurance over 5000 switching cycles. These structures offer great potential for diverse applications such as non-volatile memory design, neuromorphic computing, flexible electronics and optoelectronics. Their advantages lie in their fabrication process, reliable resistive switching behavior and overall performance stability.

Background Human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) coinfection has emerged as a leading cause of morbidity throughout the world in the last two decades. The coronavirus disease 2019 (COVID-19) pandemic has escalated the disease burden further by increasing the number of intravenous (IV) drug abusers and unemployment. Aim The present study was done to analyse the impact of COVID-19 on seroprevalence as well as trends during pre, post and pandemic years of coinfection and mono-infections in the Malwa region of Punjab. Setting and Design This descriptive cross-sectional study was done in the department of immunohematology and blood transfusion at a tertiary care hospital. Materials and Methods The data on transfusion-transmitted infections (TTIs) was collected for a period of four years from 2019 to 2022, that is, pre, post and during the pandemic period. All the blood samples were screened for viral markers, HIV I and II, HCV, and hepatitis B surface antigen (HBsAg) using the enzyme-linked immunosorbent assay (ELISA) technique. Malarial antigen and syphilis infection testing was done using a rapid diagnostic card test. The total number of sero-reactive cases and their distribution were noted. Results A total of 58,953 donors were screened and included during the study period. Each blood donor was identified by a donor registration number. The overall TTI seroprevalence in blood donors was 2.83% (n = 1670). The seroprevalence of TTIs in blood donors showed an increasing trend for HIV, HCV, and HBsAg in 2019–2021, whereas there was a decrease in reactivity status in the 2022 (back to pre-pandemic year). There was a significant increase in the coinfection rate from 0.1% to 0.25%. Conclusion The COVID-19 pandemic impact on the coinfection rate of TTI was significant. To curb these TTIs and coinfections, education and public awareness are the key factors. India is a developing country, so transfusion medicine specialists need to work day and night to practice safe blood services.

Background The Government of India undertook several measures for COVID-19 pandemic, of which school closure was one. This led to sudden shift of teaching methods from classroom-based to online mode. This study aimed to understand the effects of school closure on children during lockdown period. Methods This was a community-based qualitative study conducted in Faridabad District, Haryana, India, from January 2022 to February 2022. Two focus group discussions (FGDs) with school students of 8th standard and above and two FGDs with parents of school students below 8th standard were conducted. In-depth interviews were conducted with two school headmasters, two teachers, a Senior Medical Officer (SMO), District Education Officer (DEO), and District Elementary Education Officer (DEEO). Responses were recorded, transcribed, and reviewed to identify themes, and thematic analysis was done. Four major themes were framed—academic, physical, behavioral, and psychosocial. Results Decreased academic performance, teacher-student interaction, and distraction from the study were found in academic domain. There was reluctance to go back to school reopening. Low physical exercise, irregular sleep cycles meal skipping, and increased screen time were noticed. Limited mid-day meal nutrition was available. Increased fear, anxiety, and loneliness were faced along with an increase in household quarrels and violence. An increase in the number of child marriages was also reported. Few students had positive impacts like improvement in hygiene practices, learning new hobbies, and skills. Conclusion School closure had impact on the physical, mental, socio-behavioral, and academic aspects of school-going children. In case of similar events in the future, these impacts must be kept in mind by policymakers to achieve a balance between effective pandemic control and fruitful childhoods.