The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

Breast cancer (BC) screening is vital, as it is linked to a greater likelihood of survival, more effective treatment, and better quality of life. One of the most extensively applied models for conceptualizing probable barriers and facilitators to the adoption of desired health behavior is the health belief model (HBM). This study aimed to assess the impact of health perception and knowledge on breast self-examination intention (BSE) using HBM. HBM measures specific factors (perceived susceptibility, severity, barriers, benefits, cues to action, and self-efficacy) that impact one’s intention to use BSE. Data were collected from female undergraduate students (n = 680) using a cross-sectional design, stratified simple random sampling, and a self-administered structured online questionnaire. We used multivariable logistic regression to evaluate our assumptions for students who intended to self-examine for BC. For demographic factors, we modified the multivariate model. Most respondents (93%) were under the age of 24 years. Female students from health colleges (48.5%), in their fourth academic year (42.6%), and from the southern region (57.6%) accounted for the majority of the sample. Respondents with a family history of BC were 9.7% of the total. Categories for age, college, region, residency, and BSE intention showed significant differences in their average knowledge scores. The survey revealed that 72.4% were aware of abnormal breast changes. Three constructs of the health belief model (perceived benefit, perceived barriers, and self-efficacy) are good predictors of BSE intention. Theory-based behavioral change interventions are urgently required for students to improve their prevention practices. Furthermore, these interventions will be effective if they are designed to remove barriers to BSE intention, improve female students’ self-efficacy, and enlighten them on the benefits of self-examination.

The COVID-19 virus has infected millions of people and became a global pandemic in 2020. The efficacy of laboratory and clinical parameters in the diagnosis and monitoring of COVID-19 has been established. The CT scan has been identified as a crucial tool in the prognostication of COVID-19 pneumonia. Moreover, it has been proposed that the CT severity score can be utilized for the diagnosis and prognostication of COVID-19 disease severity and exhibits a correlation with laboratory findings such as inflammatory markers, blood glucose levels, and clinical parameters such as endotracheal intubation, oxygen saturation, mortality, and hospital admissions. Nevertheless, the correlation between the CT severity score and clinical or laboratory parameters has not been firmly established. The objective of this study is to provide a comprehensive review of the aforementioned association. This review used a systematic approach to collate and assess the existing literature that investigates the correlation between CT severity score and laboratory and clinical parameters. The search was conducted using Embase Ovid, MEDLINE Ovid, and PubMed databases, covering the period from inception to 20 May 2023. This review identified 20 studies involving more than 8000 participants of varying designs. The findings showed that the CT severity score is positively associated with laboratory and clinical parameters in COVID-19 patients. The findings indicate that the CT severity score exhibits a satisfactory level of prognostic accuracy in predicting mortality among patients with COVID-19.

Although seasonal changes in amygdala volume have been demonstrated in animals, seasonal differences in human amygdala subregion volumes have yet to be investigated. Amygdala volume has also been linked to depressed mood. Therefore, we hypothesized that differences in photoperiod would predict differences in amygdala or subregion volumes, and that this association would be linked to depressed mood. 10,033 participants ranging in age from 45 to 79 years, were scanned by MRI in a single location. Amygdala subregion volumes were obtained using automated processing and segmentation algorithms. A mediation analysis tested whether amygdala volume mediated the relationship between photoperiod and mood. Photoperiod was positively associated with total amygdala volume (p <.001). Multivariate (GLM) analyses revealed significant effects of photoperiod across all amygdala subregion volumes for both hemispheres (p <.001). Post-hoc univariate regression analyses revealed significant associations of photoperiod with each amygdala subregion volume (p <.001). PLS showed highest loadings of amygdala subregions in lateral nucleus, ABN, basal nucleus, CAT, PLN, AAA, central nucleus, cortical nucleus, and medial nucleus for left hemisphere, and ABN, lateral nucleus, CAT, PLN, cortical nucleus, AAA, central nucleus, and medial nucleus for right hemisphere. There were no significant associations between photoperiod and mood nor between mood scores and amygdala volumes, and due to the lack of these associations the mediation hypothesis was not supported. This study is the first to demonstrate an association between photoperiod and amygdala volume. These findings add to the evidence supporting the role of photoperiod on brain structural plasticity.

Coronavirus disease 2019 (COVID-19) is caused by a coronavirus family member known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The main laboratory test to confirm the quick diagnosis of COVID-19 infection is reverse transcription-polymerase chain reaction (RT-PCR) based on nasal or throat swab sampling. A small percentage of false-negative RT-PCR results have been reported. The RT-PCR test has a sensitivity of 50–72%, which could be attributed to a low viral load in test specimens or laboratory errors. In contrast, chest CT has shown 56–98% of sensitivity in diagnosing COVID-19 at initial presentation and has been suggested to be useful in correcting false negatives from RT-PCR. Chest X-rays and CT scans have been proposed to predict COVID-19 disease severity by displaying the score of lung involvement and thus providing information about the diagnosis and prognosis of COVID-19 infection. As a result, the current study provides a comprehensive overview of the utility of the severity score index using X-rays and CT scans in diagnosing patients with COVID-19 when compared to RT-PCR.

Background: The use of ultrasound-based measurements to determine renal size has proven valuable in the diagnosis of renal function and associated disorders. The dimensions of the abdominal organs are affected by an individual’s body age, height, sex, and weight. The objective of this study was to establish the standard sonographic parameters for renal dimensions and to determine the correlation between body measurements and renal dimensions in a population of healthy adults residing in Jazan City, Saudi Arabia. Methods: The present study was a prospective study conducted at a single center located in Jazan City from February to August 2022. Ninety-five participants underwent abdominal ultrasonography. The process of measuring renal size through sonography entails the measurement of various dimensions of the kidney, such as renal length, width, and thickness. The demographic information of the participants, including their sex, age, height, and weight, was documented. All analyses were performed using Statistical Package for the Social Sciences v27 software. Results: The dimensions of the right kidney, specifically the length, width, and thickness, had mean value of 9.79 centimeters (cm), 5.09 cm, and 4.10 cm, respectively. The left kidney had mean dimensions of 10.1 cm, 5.09 cm, and 4.10 cm for length, width, and thickness, respectively. The left kidney was larger than the right kidney. Furthermore, male participants exhibited larger kidney measurements than their female counterparts did. A noteworthy positive correlation was observed between the thickness of the left kidney and sex, whereas no significant correlations were found with age, weight, or height. Conclusion: The current study revealed that the kidney dimensions were observed to be larger in males as compared to females. The research findings indicate that there is no significant correlation between kidney dimensions and various demographic factors, such as age, height, weight, and sex. In addition, this study provides reference tables for further use.