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Bone modifying agents BMAs (oral and IV bisphosphonates, denosumab) are used to treat bone loss due to endocrine therapy in patients with hormone receptor positive (HR +) early breast cancer and non-metastatic prostate cancer (NMPC). Timely initiation of appropriate sequential therapy is imperative to reduce cancer treatment-induced bone loss (CTIBL). This narrative review summarizes current literature regarding management of CTIBL in HR + early breast cancer and NMPC patients. Risk factors for fragility fractures, screening strategies, optimal timing for the treatment, dosing/duration of therapy, and post treatment monitoring have not been clearly defined in HR + early breast and NMPC patients receiving endocrine therapy. This review aims to discuss the utility of fracture risk assessment (FRAX) tool for the prevention and management of CTIBL, osteoanabolic therapy for imminent fracture risk reduction, and sequential therapy options. Using predefined terms, PubMed, MEDLINE, and Google Scholar were searched for studies on CTIBL in HR + breast and NMPC patients. We included randomized clinical trials, meta-analysis, evidence-based reviews, observational studies, and clinical practice guidelines. Fracture risk assessment tools (FRAX) guide therapy for osteoporosis in patients with early HR + breast cancer and NMPC. BMAs to prevent bone loss should be initiated at higher T -score than recommended by FRAX in premenopausal HR + breast cancer patients with chemotherapy-induced ovarian failure, oophorectomy and gonadotropin releasing hormone (GnRH) therapy, post-menopausal women with HR + breast cancer receiving aromatase inhibitor therapy, and NMPC patients with androgen deprivation therapy. Sequential therapy with osteoanabolic agents as first line treatment offers a potential therapeutic strategy in patients with high imminent fracture risk. Due to limited data in cancer patients regarding management of osteoporosis, a dosing schedule similar to osteoporosis is considered appropriate. Risk stratification to identify vulnerable patient population, choosing the appropriate sequential therapy, and close monitoring of patients at the risk of bone loss can potentially reduce the mortality, morbidity, and health care cost related to CTIBL.

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.

The rise of anthropogenic activities has resulted in the increasing release of various contaminants into the environment, jeopardizing fragile ecosystems in the process. Heavy metals are one of the major pollutants that contribute to the escalating problem of environmental pollution, being primarily introduced in sensitive ecological habitats through industrial effluents, wastewater, as well as sewage of various industries. Where heavy metals like zinc, copper, manganese, and nickel serve key roles in regulating different biological processes in living systems, many heavy metals can be toxic even at low concentrations, such as mercury, arsenic, cadmium, chromium, and lead, and can accumulate in intricate food chains resulting in health concerns. Over the years, many physical and chemical methods of heavy metal removal have essentially been investigated, but their disadvantages like the generation of chemical waste, complex downstream processing, and the uneconomical cost of both methods, have rendered them inefficient,. Since then, microbial bioremediation, particularly the use of bacteria, has gained attention due to the feasibility and efficiency of using them in removing heavy metals from contaminated environments. Bacteria have several methods of processing heavy metals through general resistance mechanisms, biosorption, adsorption, and efflux mechanisms. Bacillus spp. are model Gram-positive bacteria that have been studied extensively for their biosorption abilities and molecular mechanisms that enable their survival as well as their ability to remove and detoxify heavy metals. This review aims to highlight the molecular methods of Bacillus spp. in removing various heavy metals ions from contaminated environments.

Chemical kinetics is a branch of chemistry that is founded on understanding chemical reaction rates. Chemical kinetics relates many aspects of cosmology, geology, and even in some cases of, psychology. There is a need for mathematical modelling of these chemical reactions. Therefore, the present research is based on chemical kinetics-based modelling and dynamics of enzyme processes. This research looks at the two-step substrate-enzyme reversible response. In the two step-reversible reactions, substrate combines with enzymes which is further converted into products with two steps. The model is displayed through the flow chart, which is then transformed into ODEs. The Atangana-Baleanu time-fractional operator and the Mittag-Leffler kernel are used to convert the original set of highly nonlinear coupled integer order ordinary differential equations into a fractional-order model. Additionally, it is shown that the solution to the investigated fractional model is unique, limited, and may be represented by its response velocity. A numerical scheme, also known as the Atangana-Toufik method, based on Newton polynomial interpolation technique via MATLAB software, is adopted to find the graphical results. The dynamics of reaction against different reaction rates are presented through various figures. It is observed that the forward reaction rates increase the reaction speed while backward reaction rates reduce it.

Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts. Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event. We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.

Salmonella enterica serovar Typhi (S. Typhi) is a Gram-negative pathogen that causes typhoid fever in humans. Though many serotypes of Salmonella spp. are capable of causing disease in both humans and animals alike, S. Typhi and S. Paratyphi are common in human hosts only. The global burden of typhoid fever is attributable to more than 27 million cases each year and approximately 200,000 deaths worldwide, with many regions such as Africa, South and Southeast Asia being the most affected in the world. The pathogen is able to cause disease in hosts by evading defense systems, adhesion to epithelial cells, and survival in host cells in the presence of several virulence factors, mediated by virulence plasmids and genes clustered in distinct regions known as Salmonella pathogenicity islands (SPIs). These factors, coupled with plasmid-mediated antimicrobial resistance genes, enable the bacterium to become resistant to various broad-spectrum antibiotics used in the treatment of typhoid fever and other infections caused by Salmonella spp. The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains in many countries of the world has raised great concern over the rise of antibiotic resistance in pathogens such as S. Typhi. In order to identify the key virulence factors involved in S. Typhi pathogenesis and infection, this review delves into various mechanisms of virulence, pathogenicity, and antimicrobial resistance to reinforce efficacious disease management.

The widespread use of fertilizers is a result of the increased global demand for food. The commonly used chemical fertilizers may increase plant growth and output, but they have deleterious effects on the soil, the environment, and even human health. Therefore, nanofertilizers are one of the most promising solutions or substitutes for conventional fertilizers. These engineered materials are composed of nanoparticles containing macro- and micronutrients that are delivered to the plant rhizosphere in a regulated manner. In nanofertilizers, the essential minerals and nutrients (such as N, P, K, Fe, and Mn) are bonded alone or in combination with nano-dimensional adsorbents. This review discusses the development of nanotechnology-based smart and efficient agriculture using nanofertilizers that have higher nutritional management, owing to their ability to increase the nutrient uptake efficiency. Additionally, the synthesis and mechanism of action of the nanofertilizers are discussed, along with the different types of fertilizers that are currently available. Furthermore, sustainable agriculture can be realised by the targeted delivery and controlled release of nutrients through the application of nanoscale active substances. This paper emphasises the successful development and safe application of nanotechnology in agriculture; however, certain basic concerns and existing gaps in research need to be addressed and resolved.

Drawing primarily from qualitative interviews conducted between 2017 and 2018, this empirical study tells a granular story of how legal actors mobilised during the Lawyers' Movement in Pakistan (2007-2009) from the perspective of lawyer-leaders who organised, steered and sustained support for the Movement through rapidly shifting political conditions. By underscoring the contribution of lawyer-leaders in empowering judges, the article seeks both to displace uncritical assumptions and arguments about courts as the nucleus of legal mobilisation in Pakistan, and to highlight the crucial role of political parties in the restoration of the judiciary against the backdrop of disintegrating lawyer-judge coalitions. Given Pakistan's political context of a 'hybrid regime', the article reflects on the unsuitability of the 'legal complex' theory of 'political liberalism' for analysing and understanding the Movement, and locates it instead in the literature on legal mobilisation in authoritarian regimes.

IntroductionAccurate seizure-recognition by clinicians is an important skill. However, experience of epilepsy is highly variable, and seizure misdiagnosis rates are high. We conducted a structured evalua- tion of EEG requests to understand how doctors conceptualize seizures and examine how key elements relate to test outcome.MethodsWe carried out a retrospective analysis of EEG referrals from general paediatric and emergency departments for first-time seizure presentation presenting to CHI-Crumlin between 2018-2020. Clinical seizure descriptors were coded. A word cloud was developed for free text entries and Chi-square and Fisher’s exact testing examined the relationship between specific seizure descriptors and EEG outcome.Results302 EEG requests were analysed. Children’s median age was 3.4 years (range 0-16). 64% described motor features. Sensory symptoms were described in 6%; affective and behavioural phenomena in 5%. Only 21% described lateralizing signs although 78% provided regional descriptors. Autonomic signs or colour change were described in 20%. Altered awareness was described in 11%. Duration of the ictus and post-ictal event was documented in 61% and 27% respectively. Epileptiform abnormalities were signifi- cantly higher in those that documented lateralizing signs, post-ictal features, and specified afebrile onset.ConclusionSeizure referrals from emergency and general paediatric doctors emphasise motor man- ifestations. Sensory, emotional, and affective symptoms are rarely described. Granular semiological detail of seizure duration, evolution, and post-ictal features is frequently lacking however, predicts EEG abnormality when provided.

Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixth‐generation (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase‐shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio‐frequency (RF) chains makes RIS an energy‐efficient and cost‐effective solution for future wireless networks. In this paper, the state‐of‐the‐art research on different aspects of RIS‐assisted communication is explored. Specifically, the fundamentals of RIS are first introduced, including the RIS's structure, operating principle, and deployment strategies. The emerging applications of RISs are then comprehensively discussed for 6G wireless networks. In addition, the crucial challenges for RIS‐assisted networks are elaborated, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase‐shift optimization, and resource allocation in RIS‐assisted networks are presented. Finally, to provide effective guidance for future research, important research directions for realizing RIS‐assisted network are highlighted.