Medicolite-Machine Learning-Based Patient Care Model.

This paper discusses the machine learning effect on healthcare and the development of an application named "Medicolite" in which various modules have been developed for convenience with health-related problems like issues with diet. It also provides online doctor appointments from home and medication through the phone. A healthcare system is "Smart" when it can decide on its own and can prescribe patients life-saving drugs. Machine learning helps in capturing data that are large and contain sensitive information about the patients, so data security is one of the important aspects of this system. It is a health system that uses trending technologies and mobile internet to connect people and healthcare institutions to make them aware of their health condition by intelligently responding to their questions. It perceives information through machine learning and processes this information using cloud computing. With the new technologies, the system decreases the manual intervention in healthcare. Every single piece of information has been saved in the system and the user can access it any time. Furthermore, users can take appointments at any time without standing in a queue. In this paper, the authors proposed a CNN-based classifier. This CNN-based classifier is faster than SVM-based classifier. When these two classifiers are compared based on training and testing sessions, it has been found that the CNN has taken less time (30 seconds) compared to SVM (58 seconds).

Machine Learning and IoT-Based Waste Management Model.

A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste-collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution-free cities.

Serum cytokine profiling and enrichment analysis reveal the involvement of immunological and inflammatory pathways in stable patients with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a major global health problem. It results from chronic inflammation and causes irreversible airway damage. Levels of different serum cytokines could be surrogate biomarkers for inflammation and lung function in COPD. We aimed to determine the serum levels of different biomarkers in COPD patients, the association between cytokine levels and various prognostic parameters, and the key pathways/networks involved in stable COPD. In this study, serum levels of 48 cytokines were examined by multiplex assays in 30 subjects (control, n=9; COPD, n=21). Relationships between serum biomarkers and forced expiratory volume in 1 second, peak oxygen uptake, body mass index, dyspnea score, and smoking were assessed. Enrichment pathways and network analyses were implemented, using a list of cytokines showing differential expression between healthy controls and patients with COPD by Cytoscape and GeneGo Metacore™ software (Thomson-Reuters Corporation, New York, NY, USA). Concentrations of cutaneous T-cell attracting chemokine, eotaxin, hepatocyte growth factor, interleukin 6 (IL-6), IL-16, and stem cell factor are significantly higher in COPD patients compared with in control patients. Notably, this study identifies stem cell factor as a biomarker for COPD. Multiple regression analysis predicts that cutaneous T-cell-attracting chemokine, eotaxin, IL-6, and stem cell factor are inversely associated with forced expiratory volume in 1 second and peak oxygen uptake change, whereas smoking is related to eotaxin and hepatocyte growth factor changes. Enrichment pathways and network analyses reveal the potential involvement of specific inflammatory and immune process pathways in COPD. Identified network interaction and regulation of different cytokines would pave the way for deeper insight into mechanisms of the disease process.

Synthesis, spectroscopic, and antimicrobial studies of binuclear metallocene (M = Ti, Zr, or Hf) derivatives of bis(mercaptoazoles).

The reactions of (eta(5) - C(5)H(5))(2)MCl(2) (M = Ti, Zr, or Hf) with mercaptoazoles (LH(2)), namely, bis(mercaptotriazoles), bis(mercap- tooxadiazoles), and bis(mercaptothiadiazoles) in 2 : 1 molar ratio, respectively, have been studied in dry tetrahydrofuran in the presence of n-butylamine and the binuclear complexes of the type [{(eta - C(5)H(5))(2) M}(2)(L)] (M = Ti/Zr/Hf) are obtained. Tentative structural conclusions are drawn for the reaction products based upon elemental analysis, electrical conductance, magnetic moment, and spectral data (UV-Vis, IR, (1)H NMR, and (13)C NMR). FAB-mass spectra of few complexes of each series were also carried out to confirm the binuclear structures. Studies were conducted to assess the growth-inhibiting potential of the complexes synthesized, and the ligands against various fungal and bacterial strains.