Artificial intelligence as a proficient tool in detecting pulmonary tuberculosis in massive population screening programs: a case study in Chennai, India.

Objective: To evaluate the performance of Genki, a computer-aided detection (CADe) software, in detecting tuberculosis (TB) using chest radiography in a mobile TB screening program in Chennai, India.

Materials And Methods: Genki, an AI-based CADe software, was employed in four mobile diagnostic units in remote areas of Chennai, India for screening TB. Patients from remote areas of Chennai who visited the vans and registered in the screening program underwent chest radiography, and the acquired X-ray scans were analyzed using Genki, which provided an assessment of each scan as either "TB suggestive" or "TB not suggestive".

Efficacy of Ultrasound-Guided Injection of Platelet-Rich Plasma in Treatment of Sports-Related Meniscal Injuries.

 Meniscal injuries are a common occurrence in sports-related activities, often leading to pain, reduced joint function, and impaired athletic performance. This study aimed to evaluate the role of ultrasound-guided intra-articular platelet-rich plasma (PRP)-rich fluid injection which was obtained through serial centrifugation in the treatment of meniscal injuries resulting from sports activities.  A prospective study was conducted involving 54 cases with grade I, II, and III meniscal injuries, aged 18 and 43 years.

A Primer on Reinforcement Learning in Medicine for Clinicians.

Reinforcement Learning (RL) is a machine learning paradigm that enhances clinical decision-making for healthcare professionals by addressing uncertainties and optimizing sequential treatment strategies. RL leverages patient-data to create personalized treatment plans, improving outcomes and resource efficiency. This review introduces RL to a clinical audience, exploring core concepts, potential applications, and challenges in integrating RL into clinical practice, offering insights into efficient, personalized, and effective patient care.

Quantifying Hand Motion Complexity in Simulated Sailing Using Inertial Sensors.

The control of hand movement during sailing is important for performance. To quantify the amount of regularity and the unpredictability of hand fluctuations during the task, the mathematical algorithm Approximate Entropy (ApEn) of the hand acceleration can be used. Approximate Entropy is a mathematical algorithm that depends on the combination of two input parameters including (1) the length of the sequences to be compared (m), and (2) the tolerance threshold for accepting similar patterns between two segments (r).

Theoretical and experimental investigation of mixed-ligand metal(II) Schiff base complexes using maleic acid as the auxiliary ligand.

This work is focused on the synthesis of several transition metal complexes [ML(MA)], where M = Copper (II), Zinc (II), Cobalt (II) and Nickel (II), MA = maleic acid and L = Schiff base generated from benzene-1,2-diamine [phenylenediamine] and 4-chlorobenzaldehyde. The characterization using Fourier-Transform Infrared, Nuclear Magnetic Resonance spectroscopy, Ultraviolet-Visible spectra, Mass, Electro Paramagnetic Resonance and elemental analysis confirm the square planar geometry of the complexes. The antimicrobial potential of the complexes has been tested by the broth dilution method and the antioxidant method has been done by free radical scavenging analysis.

Co-designing a motivational interviewing training platform to enhance oncology healthcare professional communication.

Background: Oncology healthcare professionals (HCPs) using motivational interviewing may motivate and support patients with chronic illness to adhere to medications. Research of online motivational interviewing training focusing on medication adherence in cancer is limited.

Objective: Co-design, develop, and preliminarily evaluate a motivational interviewing training platform (MITP) for oncology HCPs focused on medication adherence.

Safety and adherence to medications and self-care advice in oncology (SAMSON): pilot randomised controlled trial protocol.

Introduction: With the increasing use of oral anti-cancer medicines (OAMs), research demonstrating the magnitude of the medication non-adherence problem and its consequences on treatments' efficacy and toxicity is drawing more attention. Mobile phone interventions may be a practical solution to support patients taking OAMs at home, yet evidence to inform the efficacy of these interventions is lacking. The safety and adherence to medications and self-care advice in oncology (SAMSON) pilot randomised control trial (RCT) aims to evaluate the acceptability, feasibility and potential efficacy of a novel digital solution to improve medication adherence (MA) among people with cancer.

A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors.

The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort that often outweigh the benefits. On the other hand, enabling large numbers of IoT applications to share available third-party IoT devices, which are deployed and maintained independently by a variety of IoT device providers, reduces IoT application development costs, time, and effort.

Co-Design, Development, and Evaluation of a Mobile Solution to Improve Medication Adherence in Cancer: Design Science Research Approach.

Background: Medication nonadherence negatively impacts the health outcomes of people with cancer as well as health care costs. Digital technologies present opportunities to address this health issue. However, there is limited evidence on how to develop digital interventions that meet the needs of people with cancer, are perceived as useful, and are potentially effective in improving medication adherence.

Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury.

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively.

A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.

Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now.

DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.

Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel 'F' Flag feature for early detection. This novel 'F' indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma.

Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection.

Background: Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored.

Methods: In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs).

Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware.

One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem.

"Take the tablet or don't take the tablet?"-A qualitative study of patients' experiences of self-administering anti-cancer medications related to adherence and managing side effects.

Purpose: Medication non-adherence is a well-recognised problem in cancer care, negatively impacting health outcomes and healthcare resources. Patient-related factors influencing medication adherence (MA) are complicated and interrelated. There is a need for qualitative research to better understand their underlying interaction processes and patients' needs to facilitate the development of effective patient-tailored complex interventions.

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.

Background: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models.

Wireless electrical-molecular quantum signalling for cancer cell apoptosis.

Quantum biological tunnelling for electron transfer is involved in controlling essential functions for life such as cellular respiration and homoeostasis. Understanding and controlling the quantum effects in biology has the potential to modulate biological functions. Here we merge wireless nano-electrochemical tools with cancer cells for control over electron transfer to trigger cancer cell death.

Development of a person-centred digital platform for the long-term support of people living with an adult-onset genetic disease predisposition: a mixed-methods study protocol.

Introduction: Individuals at an inherited high-risk of developing adult-onset disease, such as breast cancer, are rare in the population. These individuals require lifelong clinical, psychological and reproductive assistance. After a positive germline test result, clinical genetic services provide support and care coordination.

Targeting stromal cell sialylation reverses T cell-mediated immunosuppression in the tumor microenvironment.

Immunosuppressive tumor microenvironments (TMEs) reduce the effectiveness of immune responses in cancer. Mesenchymal stromal cells (MSCs), precursors to cancer-associated fibroblasts (CAFs), promote tumor progression by enhancing immune cell suppression in colorectal cancer (CRC). Hyper-sialylation of glycans promotes immune evasion in cancer through binding of sialic acids to their receptors, Siglecs, expressed on immune cells, which results in inhibition of effector functions.

Weed Detection Using Deep Learning: A Systematic Literature Review.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds.