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Background Workforce planning in the British healthcare system (NHS) is associated with significant costs of agency staff employment. The introduction of a novel software (ABG) as a ‘people to people economy’ (P2PE) platform for temporary staff recruitment offers a potential solution to this problem. Consequently, the focus of this study was twofold – primarily to explore the locum doctor landscape, and secondarily to evaluate the implementation of P2PE in the healthcare industry. Methods Documentary analysis was conducted alongside thirteen semi structured interviews across five informant groups: two industry experts, two healthcare consultants, an executive director, two speciality managers and six doctors. Results We found that locum doctors are indispensable to covering workforce shortages, yet existing planning and recruitment practices were found to be inefficient, inconsistent and lacking transparency. Contrarily, mobile-first solutions such as ABG seem to secure higher convenience, better transparency, cost and time efficiency. We also identified factors facilitating the successful diffusion of ABG; these were in line with classically cited characteristics of innovation such as trialability, observability, and scope for local reinvention. Drawing upon the concept of value-based healthcare coupled with the analysis of our findings led to the development of Information Exchange System (IES) model, a comprehensive framework allowing a thorough comparison of recruitment practices in healthcare. Conclusion IES was used to evaluate ABG and its diffusion against other recruitment methods and ABG was found to outperform its alternatives, thus suggesting its potential to solve the staffing and cost crisis at the chosen hospital.

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data–based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages — stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 — minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 — mRMR, mutual information (MI) and CFS, and Pipeline 3 — mRMR and support vector machine–recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

We collected data from 10 caves (3 sites) in the North and Middle Andaman Islands to determine the spatiotemporal changes in the roosting pattern of the Andaman populations of Edible-nest Swiftlet (Aerodramus fuciphagus inexpectatus). This echolocating diurnal aerial forager showed temporal variation in its round-the-clock entry and exit patterns. With spatiotemporal variations (site-wise, cave-wise, hourly, and monthly), more than 98% of birds returned daily to the roosting caves between 1700 h and 2000 h. However, their daily departure time (between 0400 h and 0700 h) did not vary spatially (site-wise and cave-wise). The movements of birds at the cave openings were higher during the nestling period in April and May. The daily roosting period inside the caves (mean 525.20 min; SD 82.98) also showed spatiotemporal variation. Day length affected movement of the birds before and after sunset and sunrise. We conclude that roosting movement of the Andaman Edible-nest Swiftlet varied spatiotemporally in the Andaman Islands. This first detailed description of such variation in the roosting patterns of the species will stimulate further exploration of the various biological and environmental factors affecting movements of this cave-dwelling endemic. Received 2 December 2017. Accepted 18 September 2018.

We collected data from 10 caves (3 sites) in the North and Middle Andaman Islands to determine the spatiotemporal changes in the roosting pattern of the Andaman populations of Edible-nest Swiftlet (Aerodramus fuciphagus inexpectatus). This echolocating diurnal aerial forager showed temporal variation in its round-the-clock entry and exit patterns. With spatiotemporal variations (site-wise, cave-wise, hourly, and monthly), more than 98% of birds returned daily to the roosting caves between 1700 h and 2000 h. However, their daily departure time (between 0400 h and 0700 h) did not vary spatially (site-wise and cave-wise). The movements of birds at the cave openings were higher during the nestling period in April and May. The daily roosting period inside the caves (mean 525.20 min; SD 82.98) also showed spatiotemporal variation. Day length affected movement of the birds before and after sunset and sunrise. We conclude that roosting movement of the Andaman Edible-nest Swiftlet varied spatiotemporally in the Andaman Islands. This first detailed description of such variation in the roosting patterns of the species will stimulate further exploration of the various biological and environmental factors affecting movements of this cave-dwelling endemic.

The preservation of food by drying is one of the most commonly used methods in the food processing industry. Watermelon belongs to the family of Cucurbitaceae .The processing of watermelon generates a large amount of waste in the form of rind, peel and seeds. The watermelon rind is an under- utilised waste generated after the consumption of the fruit. Watermelon rind contains vitamin C, dietary fiber, citrulline, potassium, a small amount of vitamin B-6. It is also known to contain a variety of bioactive compounds like cucurbitacin, triterpenes, sterols and alkaloid. The citrulline in watermelon rind gives it antioxidant effects that protect the body from free-radical damage. In this study efforts have been made to dry and preserve the watermelon rind by hot air drying and freeze drying technique. The water absorption capacity, rehydration ratio and the solubility of the freeze dried watermelon rind powder were found to be 11.42±0.6 (g/g), 9±0.8 (g/g) and 8±0.5% respectively. Hot air drying significantly reduced the water absorption capacity of the watermelon rind while it increased the water solubility and bulk density. The tannins ,alkaloids and saponin content were found to be lesser in hot iar dried samples compared to freeze dried.

A Provable Data Possession (PDP) scheme allows a client which has stored data at an untrusted server to verify that the server possesses the original data that it stored without retrieving the entire file. In this thesis study, a new PDP scheme is built using the concept of sigma protocols. The client pre-processes a file and stores it on the server. At a later time, the client issues a challenge to the server requesting it to compute a Proof of Possession. The client verifies the response using its locally stored metadata. The challenge-response protocol that is derived from the sigma protocol, minimizes both computation and communication complexity. Implementation and complexity analysis of the algorithms used in the Σ-PDP scheme was done as a part of this thesis. The main goal of this research was to minimize computation and communication complexity of Σ-PDP scheme as compared to the existing PDP schemes. The main goal of this research was to minimize computation and communication complexity of Σ-PDP scheme as compared to the existing PDP schemes.

A recessive X-linked illness called Duchenne muscular dystrophy (DMD) is characterized by increasing muscle weakening and degradation. It primarily affects boys and is one of the most prevalent and severe forms of muscular dystrophy. Mutations in the gene, which codes for the essential protein dystrophin, which aids in maintaining the stability of muscle cell membranes during contraction, are the cause of the illness. Dystrophin deficiency or malfunction damages muscle cells, resulting in persistent inflammation and progressive loss of muscular mass. The pathophysiology and genetic foundation of DMD are thoroughly examined in this review paper, focusing on the function of the NF-κB signaling system in the disease's progression. An important immune response regulator, NF-κB, is aberrantly activated in DMD, which exacerbates the inflammatory milieu in dystrophic muscles. Muscle injury and fibrosis are exacerbated and muscle regeneration is hampered by the pro-inflammatory cytokines and chemokines that are produced when NF-κB is persistently activated in muscle cells. The paper also examines our existing knowledge of treatment approaches meant to inhibit the progression of disease by modifying NF-κB signaling. These include new molecular techniques, gene treatments, and pharmacological inhibitors that are intended to lessen inflammation and improve muscle healing. Furthermore covered in the analysis is the significance of supportive care for DMD patients, including physical therapy and corticosteroid treatment, in symptom management and quality of life enhancement. The article seeks to provide a thorough understanding of the mechanisms causing DMD, possible therapeutic targets, and developing treatment options by combining recent research findings. This will provide clinicians and researchers involved in DMD care and research with invaluable insights.

A COVID-19 patient often presents with multiple comorbidities and is associated with adverse outcomes. A comprehensive assessment of the prevalence of comorbidities in patients with COVID-19 is essential. This study aimed to assess the prevalence of comorbidities, severity and mortality with regard to geographic region, age, gender and smoking status in patients with COVID-19. A systematic review and multistage meta-analyses were reported using PRISMA guidelines. PubMed/MEDLINE, SCOPUS, Google Scholar and EMBASE were searched from January 2020 to October 2022. Cross-sectional studies, cohort studies, case series studies, and case–control studies on comorbidities reporting among the COVID-19 populations that were published in English were included. The pooled prevalence of various medical conditions in COVID-19 patients was calculated based on regional population size weights. Stratified analyses were performed to understand the variations in the medical conditions based on age, gender, and geographic region. A total of 190 studies comprising 105 million COVID-19 patients were included. Statistical analyses were performed using STATA software, version 16 MP (StataCorp, College Station, TX). Meta-analysis of proportion was performed to obtain pooled values of the prevalence of medical comorbidities: hypertension (39%, 95% CI 36–42, n = 170 studies), obesity (27%, 95% CI 25–30%, n = 169 studies), diabetes (27%, 95% CI 25–30%, n = 175), and asthma (8%, 95% CI 7–9%, n = 112). Moreover, the prevalence of hospitalization was 35% (95% CI 29–41%, n = 61), intensive care admissions 17% (95% CI 14–21, n = 106), and mortality 18% (95% CI 16–21%, n = 145). The prevalence of hypertension was highest in Europe at 44% (95% CI 39–47%, n = 68), obesity and diabetes at 30% (95% CI, 26–34, n = 79) and 27% (95%CI, 24–30, n = 80) in North America, and asthma in Europe at 9% (95% CI 8–11, n = 41). Obesity was high among the ≥ 50 years (30%, n = 112) age group, diabetes among Men (26%, n = 124) and observational studies reported higher mortality than case–control studies (19% vs. 14%). Random effects meta-regression found a significant association between age and diabetes ( p  < 0.001), hypertension ( p  < 0.001), asthma ( p  < 0.05), ICU admission ( p  < 0.05) and mortality ( p  < 0.001). Overall, a higher global prevalence of hypertension (39%) and a lower prevalence of asthma (8%), and 18% of mortality were found in patients with COVID-19. Hence, geographical regions with respective chronic medical comorbidities should accelerate regular booster dose vaccination, preferably to those patients with chronic comorbidities, to prevent and lower the severity and mortality of COVID-19 disease with novel SARS-CoV-2 variants of concern (VOC).

Background and objectivesWe previously reported that transforming growth factor-beta 1 (TGF-β1) promoted breast cancer progression and metastasis through inhibiting the expression of miR-4638-3p via directly targeting activating transcription factor 3 (ATF3). The present study aimed to elucidate the mechanisms of TGF-β1 downregulating ATF3 targeting miR-4638-3p via circRNA in MDA-MB231 cells.MethodsThree triple-negative human breast cancer cells (MDA-MB231, MDA-MB468, and MDA-MB453) were employed. In-silico analyses were used to identify the list of circRNAs targeting miR-4638-3p. RT-qPCR was performed to determine the expression of shortlisted circRNAs. Transient transfection and western blot analyses were carried out to identify the functional role of circ_DISP3. A dual-luciferase reporter assay was used to identify the direct binding of circ_DISP3 and miR-4638-3p.ResultsThere was an inverse correlation between the expression of circ_DISP3 and miR-4638-3p in these cells. Antisense oligos-mediated silencing of circ_DISP3 restored the function of miR-4638-3p, and downregulated ATF3 in MDA-MB231 cells. The luciferase reporter assay identified the direct binding of circ_DISP3 to miR-4638-3p in these cells.ConclusionsTGF-β1 influences the expression of ATF3 to stimulate circ_DISP3 to sponge miR-4638-3p in hBC cells. Hence, we suggest that the circ_DISP3/miR-4638-3p/ATF3 axis regulated by TGF-β1 may have potential applications for bone-metastatic breast cancer.