Today, speech synthesis is a part of our daily lives in computers all around the world. Central Kurdish Speech Corpus Construction is a speech corpus that is a primary data source for developing a speech system. There are still two main issues that prevent them from achieving the best possible performance, the lack of efficiency in training and analysis, and the difficulty in modelling. The biggest obstacle against text-to-speech in the Kurdish language is that there is a lack of text and speech recognition tools compounded by the fact that around 30 million people speak the Kurdish language in different countries. To address this issue, this corpus introduced a large vocabulary of Kurdish Text-to-Speech Dataset (KTTS, Gigant), including a pronunciation lexicon and speech corpus for the Central Kurdish dialect. A variety of subjects is comprised to record these sentences. The sentences are recorded in a voice recording studio by a Kurdish man who is a dubber. The goal of the speech corpus is to create a collection of sentences that accurately reflect the real data about the Central Kurdish dialect. A combination of audio and visual sources is used to record the 6,078 sentences of 12 document topics. They were recorded in a controlled environment using microphones that were not noisy. The total record duration is 13.63 h. The recorded sentences are in the \".wav\" format.

UNASSIGNED: Diarrhea is a significant health problem in the Third World. Identification of the pathogen that causes diarrhea is vital for measures to prevent and control this disease. There are also very few reports of diarrhea in Sudan. Our study aimed to determine the Prevalence of specific protozoan pathogens ( Entamoeba histolytica, Cryptosporidium parvum., and Giardia spp) in children in Khartoum, Sudan.
UNASSIGNED: We conducted a cross-sectional survey among children under five years of age who were hospitalized with acute diarrhea between April and December 2014. Diarrheal stool samples were collected and E. histolytica, C. parvum, and Giardia spp were examined using multiplex real-time PCR.
UNASSIGNED: Four hundred and thirty-seven children with acute diarrheawere included in this study; the higher Prevalence of diarrhea was in the age less than ≤ 2 years old (403,92.2%). The male-to-female ratio in this study was 1:1.7. infection with intestinal parasite was found in 155 (35.5%) cases, and co-infection was detected in 16 (10.3%) cases. Giardia spp(18.8%) and C. parvum (15.8 %) were the most frequently identified parasites, followed by E. histolytica (0.9). The parasite infection rate was highest and lowest in the under 2-year-old group (92.3%) and the 2-4-year-old group (7.3%). The infection rate was higher in boys (67.1%) than in girls (32.9%). The incidence of protozoan infection was higher in the rainy season (August to December) (92.2%), corresponding with that in the dry Season (April to June). (7.8%).
UNASSIGNED: Our present study demonstrated the high prevalence of Giardia spp and C. parvum in children with diarrhea in the Khartoum region and the usefulness of the multiplex real-time method in disclosing pathogenic protozoal agents. Our result highlighted the necessity of developing intervention measurement and control strategies to deal with childhood parasitic diarrhea in this region.

Chronic kidney disease (CKD) is a global public health problem with major human and economic consequences. Despite advances in clinical guidelines, classification systems and evidence-based treatments, CKD remains underdiagnosed and undertreated and is predicted to be the fifth leading cause of death globally by 2040. This review aims to identify barriers and enablers to the effective detection, diagnosis, disclosure and management of CKD since the introduction of the Kidney Disease Outcomes Quality Initiative (KDOQI) classification in 2002, advocating for a renewed approach in response to updated Kidney Disease: Improving Global Outcomes (KDIGO) 2024 clinical guidelines. The last two decades of improvements in CKD care in the UK are underpinned by international adoption of the KDIGO classification system, mixed adoption of evidence-based treatments and research informed clinical guidelines and policy. Interpretation of evidence within clinical and academic communities has stimulated significant debate of how best to implement such evidence which has frequently fuelled and frustratingly forestalled progress in CKD care. Key enablers of effective CKD care include clinical classification systems (KDIGO), evidence-based treatments, electronic health record tools, financially incentivised care, medical education and policy changes. Barriers to effective CKD care are extensive; key barriers include clinician concerns regarding overdiagnosis, a lack of financially incentivised care in primary care, complex clinical guidelines, managing CKD in the context of multimorbidity, bureaucratic burden in primary care, underutilisation of sodium-glucose co-transporter-2 inhibitor (SGLT2i) medications, insufficient medical education in CKD, and most recently - a sustained disruption to routine CKD care during and after the COVID-19 pandemic. Future CKD care in UK primary care must be informed by lessons of the last two decades. Making step change, over incremental improvements in CKD care at scale requires a renewed approach that addresses key barriers to detection, diagnosis, disclosure and management across traditional boundaries of healthcare, social care, and public health. Improved coding accuracy in primary care, increased use of SGLT2i medications, and risk-based care offer promising, cost-effective avenues to improve patient and population-level kidney health. Financial incentives generally improve achievement of care quality indicators - a review of financial and non-financial incentives in CKD care is urgently needed.

Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method to resolve biological macromolecules. In a cryo-EM experiment, the microscope produces images called micrographs. Projections of the molecule of interest are embedded in the micrographs at unknown locations, and under unknown viewing directions. Standard imaging techniques first locate these projections (detection) and then reconstruct the 3-D structure from them. Unfortunately, high noise levels hinder detection. When reliable detection is rendered impossible, the standard techniques fail. This is a problem, especially for small molecules. In this paper, we pursue a radically different approach: we contend that the structure could, in principle, be reconstructed directly from the micrographs, without intermediate detection. The aim is to bring small molecules within reach for cryo-EM. To this end, we design an autocorrelation analysis technique that allows one to go directly from the micrographs to the sought structures. This involves only one pass over the micrographs, allowing online, streaming processing for large experiments. We show numerical results and discuss challenges that lay ahead to turn this proof-of-concept into a complementary approach to state-of-the-art algorithms.

BACKGROUND: Early detection and prevention of type 2 diabetes and its complications are global health priorities. Optimal outcomes depend on individual awareness and proactive self-management of health risks. This study evaluates the effectiveness of a community-based diabetes detection and intervention program in a high-risk area in western Sydney, Australia.
METHODS: We collaborated with the Workers Lifestyle Group, Tamil Association Arts and Culture Association, and the National Aboriginal and Islanders Day Observance Committee to implement our program. Participants underwent HbA1C testing via point-of-care blood spot testing. They received personalized feedback, education on diabetes management, and were offered opportunities to enrol in lifestyle modification programs. Participants identified with pre-diabetes (HbA1C 5.7-6.4%) or diabetes (HbA1C > 6.4%) were advised to consult their General Practitioners (GPs). A follow-up questionnaire was distributed 3-8 months post-intervention to evaluate the programs usefulness and relevance and lifestyle changes implemented by the participants.
RESULTS: Over eight months, 510 individuals participated. Of these, 19% had an HbA1C > 6.4%, and 38% had levels between 5.7 and 6.4%. Among those with diabetes, HbA1C levels ranged as follows: 56% <7%; 20% 7-7.9%; 18% 8-8.9%; and 5% >9%. Post intervention survey indicated that the program was well-received, with 62.5% of responses reporting lifestyle changes and 36.3% seeking further advice from their local healthcare providers.
CONCLUSIONS: The study demonstrates a significant prevalence of pre-diabetes and diabetes in the community, similar to findings from larger-scale hospital and general practice studies. Point-of-care testing combined with personalized education effectively motivated participants toward healthier lifestyle choices and medical consultations. The paper discusses the scalability of this approach for broader population.

UNASSIGNED: To help radiologists examine the growing number of computed tomography (CT) scans, automatic anomaly detection is an ongoing focus of medical imaging research. Radiologists must analyze a CT scan by searching for any deviation from normal healthy anatomy. We propose an approach to detecting abnormalities in axial 2D CT slice images of the brain. Although much research has been done on detecting abnormalities in magnetic resonance images of the brain, there is little work on CT scans, where abnormalities are more difficult to detect due to the low image contrast that must be represented by the model used.
UNASSIGNED: We use a generative adversarial network (GAN) to learn normal brain anatomy in the first step and compare two approaches to image reconstruction: training an encoder in the second step and using iterative optimization during inference. Then, we analyze the differences from the original scan to detect and localize anomalies in the brain.
UNASSIGNED: Our approach can reconstruct healthy anatomy with good image contrast for brain CT scans. We obtain median Dice scores of 0.71 on our hemorrhage test data and 0.43 on our test set with additional tumor images from publicly available data sources. We also compare our models to a state-of-the-art autoencoder and a diffusion model and obtain qualitatively more accurate reconstructions.
UNASSIGNED: Without defining anomalies during training, a GAN-based network was used to learn healthy anatomy for brain CT scans. Notably, our approach is not limited to the localization of hemorrhages and tumors and could thus be used to detect structural anatomical changes and other lesions.

Infant meningitis remains a severe burden on global health, particularly for young infants. Traditional ultrasound imaging techniques are limited in spatial resolution to visualize white blood cells (WBCs) in the cerebrospinal fluid (CSF), which is considered a well-established marker for meningitis detection. This work presents a novel platform that uses high-resolution ultrasound to detect the backscatter signals from microscopic CSF WBCs through the anterior fontanelle of neonates and young infants. The whole system was built around a custom probe that allows for a 20 MHz focused transducer to be mechanically controlled to map the area of interest in the CSF. Data processing can be performed internally in the device without the need to extract the images for further analysis. The in vitro feasibility of the proposed solution was evaluated in imaging 7 μm particle suspensions at different concentrations relevant to meningitis diagnosis ranging from 7- to 646-particles (pp)/μL. The experimental tests were conducted from a simple setup using a sample container to a more realistic setup based on an anatomical phantom of the neonatal head. The results show high-quality images, where 7 μm particles can be resolved for the different concentrations.

The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine the optimal acquisition frequency. The brain zones implicated in motor imagery movement were analyzed, with the aim of improving wearable-EEG comfort and portability. Two detection algorithms with different configurations were implemented. The detection output was classified using a tool with various classifiers. The results were categorized into three groups to discern differences between general hand movements and no movement; specific movements and no movement; and specific movements and other specific movements (between five different finger movements and no movement). Testing was conducted on the sampling frequencies, trials, number of electrodes, algorithms, and their parameters. The preferred algorithm was determined to be the FastICACorr algorithm with 20 components. The optimal sampling frequency is 1 kHz to avoid adding excessive noise and to ensure efficient handling. Twenty trials are deemed sufficient for training, and the number of electrodes will range from one to three, depending on the wearable EEG\'s ability to handle the algorithm parameters with good performance.

UNASSIGNED: Oral cancer is significantly high in India, and screening is an effective approach to downstage the disease. Educating Community Health Workers (CHWs) on early oral cancer detection is an effective step toward reducing the burden and serves as a first step toward facilitating the transfer of knowledge. Therefore, the purpose of this hands-on education was to equip CHWs with insight on the advanced diagnostics, preventive techniques, and innovations for the early detection of oral cancer.
UNASSIGNED: A total of 178 participants were trained in two groups: Group 1 received training for screening and primary prevention, while group 2 received training on updates in recent diagnostic adjuncts and innovations, AI-enabled point-of-care diagnostics, and essential patient care in management of Oral Potentially Malignant Disorders (OPMDs). Pre- and post-assessment questionnaires were used to evaluate the participants.
UNASSIGNED: The knowledge assessment scores between the pre- and post-tests showed a statistically significant difference (p < 0.001), with rise in mean score of 3.99 from baseline. Six months following training, knowledge retention revealed a statistically significant difference (p < 0.001) in the participants\' ability to recall the information.
UNASSIGNED: A well-structured training module can create awareness, impart knowledge and upskill the CHWs for early detection of oral cancer. Retraining of CHWs is required for knowledge retention post-training.

The emergence of rice black-streaked dwarf virus (RBSDV) poses a significant threat to global cereal crop cultivation, necessitating the urgent development of reliable detection and quantification techniques. This study introduces a reliable approach for the precise and sensitive quantification of the RBSDV in cereal crop samples, employing a reverse transcription digital polymerase chain reaction (RT-dPCR) assay. We assessed the specificity and sensitivity of the RT-dPCR assay proposed for precise RBSDV detection and quantification. Our findings demonstrate that RT-dPCR was specific for detection of RBSDV, with no cross-reactivity observed with other viruses infecting cereal crops. The RT-dPCR sensitivity was over 10 times that of RT-quantitative PCR (RT-qPCR). The detection limit of RT-dPCR was 0.096 copies/μl. In addition, evaluation of RT-dPCR assay with field samples was conducted on 60 different cereal crop samples revealed that RT-dPCR (45/60) exhibited superior accuracy compared with RT-qPCR (23/60). In this study, we present a specific and accurate RT-dPCR assay for the detection and quantification of RBSDV.