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[...]these reports do not reflect the true global public health burden of ESC treatment failures, since surveillance data from resource-constrained settings are scarce. [...]AMR surveillance is not simply a research project. [...]in most countries, it should not require approval from an ethical committee because the collection and antimicrobial-susceptibility testing of gonococcal isolates are part of standard care, and no patient identification information is made available in AMR surveillance. [...]there are significant delays in the release of AMR data from the current GASPs, which limits their value as part of an early warning system for AMR emergence and limits their usefulness for informing prompt refinements of gonorrhea management guidelines and public health policy. [...]high-throughput genomics is revolutionizing our understanding of AMR and the spread of AMR gonococcal strains, as well as research aimed at improving diagnostics, AMR prediction, and vaccine development [20,63-71].

The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. Furthermore, the majority of current approaches have approached feature selection as a single objective issue. This research presents an enhanced multi-objective whale optimisation algorithm-based feature selection technique as a solution. To mine optimal feature sets, the suggested technique makes use of a unique variation known as the enhanced multi-objective whale optimisation algorithm. To verify the optimisation capability, the suggested variation has been evaluated on 10 common multi-objective CEC2009 benchmark functions. Furthermore, by comparing five classifiers in terms of accuracy, mean number of selected features, and calculation time, the effectiveness of the suggested strategy is verified against three other feature-selection techniques already in use. The experimental findings show that, when compared to the other approaches under consideration, the suggested method performed better on the assessed parameters.

The study was conducted to monitor concentration of six toxic metals in faecal pellets of blue rock pigeon from six industrial areas of Jaipur, i.e. Jhotwara, Malviya, Mansarover, Sitapura, Vishwakarma industrial areas and Sanganer industries, by using atomic absorption spectrophotometer. At the same time, trace metals were also analysed for a reference area so that a comparative impact of the metal pollution from the industries could be figured out. From the data obtained, the highest Cd, Cu, Ni, Pb and Zn concentrations were found in the faecal pellets from Sitapura industrial area (1.12 ± 0.60 μg/g; 128.9 ± 11.10 μg/g; 17 ± 2.87 μg/g; 79.6 ± 42.69 μg/g; 2250 ± 1390.16 μg/g) and the highest Cr (18.6 ± 3.65 μg/g) from Jhotwara industrial area. However, industrial environment has high deposition of studied trace metals (except Ni for Sanganer industries) as compared with reference area. The concentration of Cd was below the detectable level in faecal pellets from reference area. The trend of trace metals from Malviya, Sitapura, Jhotwara and reference areas was noticed as Zn > Cu > Pb > Cr > Ni > Cd and almost same for Sanganer industries and Mansarover industrial area as Zn > Cu > Cr > Pb > Ni > Cd. The excretion of heavy metals in fa1ecal pellets from Vishwakarma industrial area was noticed as Zn > Cr > Cu > Pb > Ni > Cd. Further levels of metals were varied significantly among different industrial areas, P  ≤ 0.05, as well as between industrial areas and reference area. The observed high concentration of different heavy metals in faecal pellets of bird species shows their exposure to metal contamination which is warning to spiked environmental pollution.

Most of the complex systems consist of multiple subsystems and can be modeled as multilayer networks. These networks are prone to random or strategical attacks and end up disintegrating the entire multilayer network. These attacks can’t be avoided, but a model can be proposed to restore the network to make it secure and robust. In multilayer networks, each inter-layer link has its own cost (in terms of money) associated with it. The total cost of the inter-layer links is considered as the available budget. In the present work, a method is proposed to introduce the optimal number of inter-layer links (under budget constraints) to maintain the robustness of the multilayer network. For the simulation purpose, three variants of the artificial multilayer networks and data set EU-Air Transport Networks are considered. Simulation results reveal that for the multilayer network constructed with random network layers, approx. 65 % of the available budget is utilized. Nearly 70 % of the total nodes are connected to a mutually connected giant component (MCGC) via inter-layer links. However, for the Configuration model, using the almost total available budget, nearly all the nodes from the considered network layers are present in MCGC. Finally, in the case of the empirical dataset, by using approx. 75 % of the available budget, almost 80 % of the total nodes from the considered network layers are connected to MCGC via inter-layer links.

Recently, extensive use of refined sugars and artificial sweeteners has led to negative health implications. Therefore, this study explores natural or unrefined sweeteners such as honey, date syrup and jaggery as potential alternatives due to their nutritional and therapeutic properties. The study aims to optimize the amounts of honey, jaggery and date syrup to substitute the addition of sucrose for sweetness in guava nectar prepared using two processing treatments: hot filling and cold filling. The bioactive and rheological properties, mineral composition ( bioavailable iron) and storage stability of the nectar were further evaluated. During storage, the formation of 5-hydroxymethylfurfural (HMF), effects on antioxidant activity and non-enzymatic browning were monitored to assess changes in overall quality. The amount of sucrose substitution in guava nectar was optimized at mass fractions of 50, 25 and 30 % for honey, jaggery and date syrup, respectively, based on organoleptic properties. The optimized formulations showed a significant improvement in total phenolic content and radical scavenging activity. The guava nectar showed pseudo-plastic behaviour with a weak gel structure due to the dispersion of pulp particles, which contributed to its viscoelastic nature at low strain (<10 %). The substitution of sucrose with natural sweetener resulted in increased mineral content; however, the bioavailability of iron considerably decreased. During storage, degradation of ascorbic acid and colour, acceleration of non-enzymatic browning and development of 5-hydroxymethyl furfural were notably high by the end of the sixth month, but the formulations remained microbiologically stable. New products can be formulated using natural sweeteners instead of sucrose, which may offer higher nutritional and therapeutic value. However, in this study, the product could be improved by further research to reduce negative effects on quality characteristics during storage.

Recent WHO guidelines recommend dual therapy with ceftriaxone or cefixime plus azithromycin for gonorrhea. Azithromycin in combination with gentamicin or spectinomycin has been recommended in treatment failure cases. Due to emergence of multi-drug resistant (MDR) and extensively-drug resistant (XDR) Neisseria gonorrhoeae strains, it is important to look for efficacy of these combinations and also of others that might be used in future. Therefore, we aimed to evaluate in vitro synergy of 21 dual combinations including current and alternative WHO recommended treatment regimens and other dual combinations. The potential utility of in-vitro interactions of 21 combinations was investigated against 95 N. gonorrhoeae strains including 79 MDR and one XDR strain collected during March 2013 to July 2017 and fractional inhibitory concentration index (FICI) was calculated. These 21 combinations comprised of two WHO currently recommended (cefixime+azithromycin, ceftriaxone+azithromycin); two WHO recommended in treatment failure cases (azithromycin+gentamicin, spectinomycin+azithromycin) and other 17 combinations. FICI of the four WHO recommended antimicrobial combinations were higher (>1.0) than the five novel combinationbreeds (FICI range 0.603-0.951) in the study i.e. gentamicin+ertapenem, moxifloxacin+ertapenem, spectinomycin+ertapenem, azithromycin+ moxifloxacin, cefixime+gentamicin. No antagonistic effect of the above four WHO recommended combinations except spectinomycin+azithromycin (FICI = 4.25) was observed for the XDR strain. Out of above five novel combinations, four combinations produced high synergistic effects in overall 95 strains and also for the XDR strain with FICI of 0.13 to 0.38. Antagonistic effects varying from 3.2 to 12.6% were observed for 10 out of 21 tested combinations (azithromycin in combination with gentamicin and spectinomycin; ceftriaxone with moxifloxacin, gentamicin, spectinomycin and ertapenem; spectinomycin with moxifloxacin and gentamicin; cefixime and gentamicin combination with moxifloxacin). WHO recommended cefixime+azithromycin, ceftriaxone+azithromycin combinations having no antagonism indicates their continuing clinical utility. Highest antagonism without any synergistic effect for the WHO recommended spectinomycin+azithromycin in treatment failure cases suggests that this combination should be evaluated further both in vitro and in vivo. Highest synergistic or additive effect without any antagonistic effect of the above five novel combinations suggests that these may be recommended for treatment in future.

The optimization of phenolic compound extraction from pigeon pea ( Cajanus cajan L.) husk was explored by manipulating three key process variables: aqueous ethanol concentration (20–60% v/v), extraction temperature (30–60 °C), and extraction time (2–6 h). Utilizing a Box-Behnken design under Response Surface Methodology (RSM), we meticulously optimized these variables to maximize extract yield, total phenolic content, and DPPH radical scavenging activity. The ideal extraction conditions determined via desirability function analysis were found to be 41.83% (v/v) ethanol concentration, 59.36 °C extraction temperature, and 6 h extraction time. These parameters yielded an impressive 9.67 ± 0.02% extract yield, 47.99 ± 0.60 mg GAE/g total phenolic content, and 51.24 ± 0.43% DPPH radical scavenging activity, closely aligning with predicted values. LC-MS (ESI-Q-TOF) metabolite profiling of the optimized extract revealed a rich presence of flavonoids, alkaloids, terpenoids, lignans, phenols, xanthones, and glycosides. This study underscores the valorisation potential of pigeon pea husk—a typically discarded by-product—for sustainable extraction of valuable phenolic compounds under optimal conditions.

Wireless sensor networks are network of the large number of sensors having lesser batteries. Such networks and clustering of the nodes for energy efficiency go hand in hand. Clustering protocols like low energy adaptive clustering hierarchy, stable election protocol, improved low energy adaptive clustering hierarchy etc. allow the nodes to grouped together, form clusters and transmit their information to the base station via their leader, i.e. cluster head. Selection of cluster head follows various approaches and vary from protocol to protocol. While some of the approaches use single hop communication of the cluster head with the base station, others opt for multi-hop communication. This paper describes energy effective procedure for heterogeneous network consisting of super, advance and normal nodes. The protocol operates according to the type of the nodes. For the high energy super nodes, the protocol follows centralized procedure and for other nodes it follows decentralized approach. The selection of cluster head among the super nodes is optimized by the use of cost function depending upon distance of the nodes from the centroid of their residual energies and their signal to interference plus noise ratio. The nodes other than super nodes achieve lesser energy consumption by using the mobile agents to transfer their data to the base station. The proposed protocol has shown improvements in terms of throughput and number of dead nodes as equated to other state-of-the-art protocols.

Background: Nipah virus (NiV) first emerged in 1998 in Malaysia, causing an outbreak of respiratory illness and encephalitis in pigs. Pig-to-human transmission of NiV associated with severe febrile encephalitis was described, and it was thought to occur through close contact with infected animals. The first outbreak was reported in India in Siliguri, West Bengal in 2001 followed by Nadia, West Bengal and adjoining areas of Bangladesh in 2007, where an intermediate animal host was not identified, suggesting bat-to-human and human-to-human transmissions. Although it is extremely difficult to document the spillover event and ascertain crossing of trans-natural boundaries by bats and bringing new viruses in an unexposed population, efforts for source identification are important to understand the epidemiology of disease. As the disease transcends beyond one species and has shown to infect humans, it therefore requires the ‘One Health approach’ in which multiple sectors coordinate and work together to achieve better public health outcomes.Objective:We summarize the re-emergence and response of the Nipah virus outbreaks (NiVD) in Kerala, India, about 1800 kms away, a decade later in 2018 and 2019. The paper recapitulates involvement of various stakeholders from the Ministry of Health and Family Welfare, Directorate of Health Research, Indian Council of Agricultural Research, State Health Department, State Animal Husbandry, District Administration, and multidisciplinary response mechanism during the NiVD outbreaks of 2018 and 2019.Methods: Information was collected from the Press Information Bureau (PIB), media/weekly alerts from the Integrated Disease Surveillance Programme (IDSP), news articles from print and electronic media, newsletters, advisories from the National Centre for Disease Control (NCDC), Disease Outbreak News (DON), World Health Organization (WHO), and published papers from various stakeholders.Findings & Conclusion:The evidence of NiV in humans and bats, with samples collected from the outbreak sites, was laboratory confirmed. The multidisciplinary response mechanisms during the 2018 outbreak helped in further understanding the importance of the One Health approach for systemic and streamlined response utilizing existing surveillance systems. This was of utmost help in the subsequent outbreak of the disease that occurred during 2019, wherein there was no documented spread of disease from the index case and no mortality was observed. This success reiterates the need for institutionalizing the involvement and cooperation of various departments and organizations during public health emergencies, especially of Zoonotic diseases, using the One Health approach.

Objective: The aim of the present work was to propose and implement deep neural network (DNN)-based handheld diagnosis system for more accurate diagnosis and severity assessment of individuals with autism spectrum disorder (ASD). Methods: Initially, the learning of the proposed system for ASD diagnosis was performed by implementing DNN algorithms such as a convolutional neural network (CNN) and long short-term memory (LSTM), and multilayer perceptron (MLP) with DSM-V based acquired dataset. The performance of the DNN algorithms was analyzed based on parameters viz. accuracy, loss, mean squared error (MSE), precision, recall, and area under the curve (AUC) during the training and validation process. Later, the optimum DNN algorithm, among the tested algorithms, was implemented on handheld diagnosis system (HDS) and the performance of HDS was analyzed. The stability of proposed DNN-based HDS was validated with the dataset group of 20 ASD and 20 typically developed (TD) individuals. Results: It was observed during comparative analysis that LSTM resulted better in ASD diagnosis as compared to other artificial intelligence (AI) algorithms such as CNN and MLP since LSTM showed stabilized results achieving maximum accuracy in less consumption of epochs with minimum MSE and loss. Further, the LSTM based proposed HDS for ASD achieved optimum results with 100% accuracy in reference to DSM-V, which was validated statistically using a group of ASD and TD individuals. Conclusion: The use of advanced AI algorithms could play an important role in the diagnosis of ASD in today's era. Since the proposed LSTM based HDS for ASD and determination of its severity provided accurate results with maximum accuracy with reference to DSM-V criteria, the proposed HDS could be the best alternative to the manual diagnosis system for diagnosis of ASD.