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Introduction: Malaria poses a serious issue for pregnant women in African regions. It is crucial to comprehend the various factors that impact receiving IPTp during ANC checkups. These are important for the well-being of both pregnant mothers and their unborn children. Therefore, this study aims to investigate the determinants of IPTp coverage among women in Cameroon, Guinea, Mali, and Nigeria. Methodology: This cross-sectional study utilized secondary data from the Malaria Indicator Surveys (MIS) across Cameroon, Guinea, Mali, and Nigeria, focusing on women who received IPTp during pregnancy with IPTp categorized dichotomously as "Yes" for ANC visits and "No" for other visits. Chi-squared tests were used to assess associations, and binary logistic regression was conducted to calculate adjusted odds ratios, confidence intervals, and p-values. Results were summarized in tables. Results: We found IPTp coverage during ANC visits was highest in Cameroon (98.6%), followed by Guinea (97.7%), Mali (97.1%), and lowest in Nigeria (95.5%). In Guinea, rural women were less likely to receive IPTp than urban women (AOR: 0.16, 95% CI: 0.07–0.41, p<0.001). In Mali, women who received 3 or more doses were less likely to receive IPTp at ANC (AOR: 0.48, p<0.01). In Nigeria, personal transport increased IPTp uptake (AOR: 1.88, p<0.01). In Cameroon, malaria prevention messages improved IPTp coverage (AOR: 3.12, p<0.05). Conclusions: This study highlights significant disparities in IPTp uptake, with rural Mali and Guinea facing lower coverage. In Nigeria, personal transport improved IPTp uptake. Targeted interventions are needed to improve ANC services and ensure equitable IPTp access across the study regions. [ABSTRACT FROM AUTHOR]

The increasingly large size of the graphical and numerical data sets collected with modern technologies requires constant update and upgrade of the statistical models, methods and procedures to be used for their analysis in order to optimize learning and maximize knowledge and understanding. This is the case for plant CT scanning (CT: computed tomography), including applications aimed at studying leaf canopies and the structural complexity of the branching patterns that support them in trees. Therefore, we first show after a brief review, how the CT scanning data can be leveraged by constructing an analytical representation of a tree branching structure where each branch is represented by a line segment in 3D and classified in a level of a hierarchy, starting with the trunk (level 1). Each segment, or branch, is characterized by four variables: (i) the position on its parent, (ii) its orientation, a unit vector in 3D, (iii) its length, and (iv) the number of offspring that it bears. The branching structure of a tree can then be investigated by calculating descriptive statistics on these four variables. A deeper analysis, based on statistical models aiming to explain how the characteristics of a branch are associated with those of its parents, is also presented. The branching patterns of three miniature trees that were CT scanned are used to showcase the statistical modeling framework, and the differences in their structural complexity are reflected in the results. Overall, the most important determinant of a tree structure appears to be the length of the branches attached to the trunk. This variable impacts the characteristics of all the other branches of the tree.

Nuclear morphological features are potent determining factors for clinical diagnostic approaches adopted by pathologists to analyze the malignant potential of cancer cells. Considering the structural alteration of the nucleus in cancer cells, various groups have developed machine learning techniques based on variation in nuclear morphometric information like nuclear shape, size, nucleus-cytoplasm ratio and various non-parametric methods like deep learning have also been tested for analyzing immunohistochemistry images of tissue samples for diagnosing various cancers. We aim to correlate the morphometric features of the nucleus along with the distribution of nuclear lamin proteins with classical machine learning to differentiate between normal and ovarian cancer tissues. It has already been elucidated that in ovarian cancer, the extent of alteration in nuclear shape and morphology can modulate genetic changes and thus can be utilized to predict the outcome of low to a high form of serous carcinoma. In this work, we have performed exhaustive imaging of ovarian cancer versus normal tissue and developed a dual pipeline architecture that combines the matrices of morphometric parameters with deep learning techniques of auto feature extraction from pre-processed images. This novel Deep Hybrid Learning model, though derived from classical machine learning algorithms and standard CNN, showed a training and validation AUC score of 0.99 whereas the test AUC score turned out to be 1.00. The improved feature engineering enabled us to differentiate between cancerous and non-cancerous samples successfully from this pilot study.

Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) is the causative agent of COVID-19 pandemic, which has resulted in global fatalities since late December 2019. Alkaloids play a significant role in drug design for various antiviral diseases, which makes them viable candidates for treating COVID-19. To identify potential antiviral agents, 102 known alkaloids were subjected to docking studies against the two key targets of SARS-CoV-2, namely the spike glycoprotein and main protease. The spike glycoprotein is vital for mediating viral entry into host cells, and main protease plays a crucial role in viral replication; therefore, they serve as compelling targets for therapeutic intervention in combating the disease. From the selection of alkaloids, the top 6 dual inhibitory compounds, namely liensinine, neferine, isoliensinine, fangchinoline, emetine, and acrimarine F, emerged as lead compounds with favorable docked scores. Interestingly, most of them shared the bisbenzylisoquinoline alkaloid framework and belong to Nelumbo nucifera, commonly known as the lotus plant. Docking analysis was conducted by considering the key active site residues of the selected proteins. The stability of the top three ligands with the receptor proteins was further validated through dynamic simulation analysis. The leads underwent ADMET profiling, bioactivity score analysis, and evaluation of drug-likeness and physicochemical properties. Neferine demonstrated a particularly strong affinity for binding, with a docking score of -7.5025 kcal/mol for main protease and -10.0245 kcal/mol for spike glycoprotein, and therefore a strong interaction with both target proteins. Of the lead alkaloids, emetine and fangchinoline demonstrated the lowest toxicity and high LD50 values. These top alkaloids, may support the body's defense and reduce the symptoms by their numerous biological potentials, even though some properties naturally point to their direct antiviral nature. These findings demonstrate the promising anti-COVID-19 properties of the six selected alkaloids, making them potential candidates for drug design. This study will be beneficial in effective drug discovery and design against COVID-19 with negligible side effects. [ABSTRACT FROM AUTHOR]

BACKGROUND:A primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions. New insights from the fusion and synthesis of such diverse patient-reported information, as expressed throughout the patient journey from diagnosis to treatment and recovery, can contribute towards informed decision-making on personalized healthcare delivery and the development of healthcare policy guidelines. METHODS AND FINDINGS:We have designed and developed an artificial intelligence based analytics framework using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups. Alongside the social interactions aspect, patient behaviours, decisions, demographics, clinical factors, emotions, as subsequently expressed over time, are extracted and analysed. More specifically, we utilised this platform to investigate the impact of online social influences on the intimate decision scenario of selecting a treatment type, recovery after treatment, side effects and emotions expressed over time, using prostate cancer as a model. Results manifest the three major decision-making behaviours among patients, Paternalistic group, Autonomous group and Shared group. Furthermore, each group demonstrated diverse behaviours in post-decision discussions on clinical outcomes, advice and expressions of emotion during the twelve months following treatment. Over time, the transition of patients from information and emotional support seeking behaviours to providers of information and emotional support to other patients was also observed. CONCLUSIONS:Findings from this study are a rigorous indication of the expectations of social media empowered patients, their potential for individualised decision-making, clinical and emotional needs. The increasing popularity of OSG further confirms that it is timely for clinicians to consider patient voices as expressed in OSG. We have successfully demonstrated that the proposed platform can be utilised to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer, in support of patient focused healthcare delivery. The platform can be extended and applied just as effectively to any other medical condition.

Androgens regulate prostate physiology, and exert their effects through the androgen receptor. We hypothesized that androgen deprivation needs additional transcription factors to orchestrate the changes taking place in the gland after castration and for the adaptation of the epithelial cells to the androgen-deprived environment, ultimately contributing to the origin of castration-resistant prostate cancer. This study was undertaken to identify transcription factors that regulate gene expression after androgen deprivation by castration (Cas). For the sake of comparison, we extended the analysis to the effects of administration of a high dose of 17β-estradiol (E2) and a combination of both (Cas+E2). We approached this by (i) identifying gene expression profiles and enrichment terms, and by searching for transcription factors in the derived regulatory pathways; and (ii) by determining the density of putative transcription factor binding sites in the proximal promoter of the 10 most up- or down-regulated genes in each experimental group in comparison to the controls Gapdh and Tbp7. Filtering and validation confirmed the expression and localized EVI1 (Mecom), NFY, ELK1, GATA2, MYBL1, MYBL2, and NFkB family members (NFkB1, NFkB2, REL, RELA and RELB) in the epithelial and/or stromal cells. These transcription factors represent major regulators of epithelial cell survival and immaturity as well as an adaptation of the gland as an immune barrier in the absence of functional stimulation by androgens. Elk1 was expressed in smooth muscle cells and was up-regulated after day 4. Evi1 and Nfy genes are expressed in both epithelium and stroma, but were apparently not affected by androgen deprivation.

The extracellular matrix of epithelial tumors undergoes structural remodeling during periods of uncontrolled growth, creating regional heterogeneity and torsional stress. How matrix integrity is maintained in the face of dynamic biophysical forces is largely undefined. Here we investigated the role of fibulin-2, a matrix glycoprotein that functions biomechanically as an inter-molecular clasp and thereby facilitates supra-molecular assembly. Fibulin-2 was abundant in the extracellular matrix of human lung adenocarcinomas and was highly expressed in tumor cell lines derived from mice that develop metastatic lung adenocarcinoma from co-expression of mutant K-ras and p53. Loss-of-function experiments in tumor cells revealed that fibulin-2 was required for tumor cells to grow and metastasize in syngeneic mice, a surprising finding given that other intra-tumoral cell types are known to secrete fibulin-2. However, tumor cells grew and metastasized equally well in Fbln2-null and -wild-type littermates, implying that malignant progression was dependent specifically upon tumor cell-derived fibulin-2, which could not be offset by other cellular sources of fibulin-2. Fibulin-2 deficiency impaired the ability of tumor cells to migrate and invade in Boyden chambers, to create a stiff extracellular matrix in mice, to cross-link secreted collagen, and to adhere to collagen. We conclude that fibulin-2 is a driver of malignant progression in lung adenocarcinoma and plays an unexpected role in collagen cross-linking and tumor cell adherence to collagen.

Women and men are different. As humans are highly visual animals, these differences should be reflected in the pattern of eye movements they make when interacting with the world. We examined fixation distributions of 52 women and men while viewing 80 natural images and found systematic differences in their spatial and temporal characteristics. The most striking of these was that women looked away and usually below many objects of interest, particularly when rating images in terms of their potency. We also found reliable differences correlated with the images' semantic content, the observers' personality, and how the images were semantically evaluated. Information theoretic techniques showed that many of these differences increased with viewing time. These effects were not small: the fixations to a single action or romance film image allow the classification of the sex of an observer with 64% accuracy. While men and women may live in the same environment, what they see in this environment is reliably different. Our findings have important implications for both past and future eye movement research while confirming the significant role individual differences play in visual attention.

IntroductionOvercrowding in the emergency departments (ED) is linked to adverse clinical outcomes, a negative impact on patient safety, patient satisfaction, and physician efficiency. We aimed to design a medical admission prediction scoring system based on readily available clinical data during ED presentation.MethodsIn this retrospective cross-sectional study, data on ED presentations and medical admissions were extracted from the Emergency and Internal Medicine departments of a tertiary care facility in Qatar. Primary outcome was medical admission.ResultsOf 320299 ED presentations, 218772 were males (68.3%). A total of 11847 (3.7%) medical admissions occurred. Most patients were Asians (53.7%), followed by Arabs (38.7%). Patients who got admitted were older than those who did not (p 60 years, female gender, discharge within the last 30 days, and worse vital signs at presentations were independently associated with higher odds of admission (p17, area under the curve (AUC) 0.831 (95% CI 0.827-0.836), and a predictive accuracy of 83.3% (95% CI 83.2-83.4). The model had a sensitivity of 69.1% (95% CI 68.2-69.9), specificity was 83.9% (95% CI 83.7-84.0), positive predictive value (PPV) 14.2% (95% CI 13.8-14.4), negative predictive value (NPV) 98.6% (95% CI 98.5-98.7) and positive likelihood ratio (LR+) 4.28% (95% CI 4.27-4.28).ConclusionMedical admission prediction scoring system can be reliably applied to the regional population to predict medical admissions and may have better generalizability to other parts of the world owing to the diverse patient population in Qatar.