Israel Health News API

{
    "totalArticles": 10856,
    "articles": [
        {
            "id": "11c1cbb49cad32043673cb184e8746b7",
            "title": "Study led by doctor killed on Oct. 7 published despite seemingly antisemitic pushback",
            "description": "Dr. Daniel Levi, an ear, nose and throat specialist at Soroka Medical Center in Beersheba, was murdered while on duty at the clinic in Kibbutz Be’eri during the Hamas assault",
            "content": "A peer-reviewed medical study led by an Israeli doctor who was killed while treating patients during the Oct. 7 Hamas attack has been published in a leading international journal, completing work he began before his death and highlighting both scient... [3874 chars]",
            "url": "https://www.ynetnews.com/health_science/article/rjhbb00ph11g",
            "image": "https://ynet-pic1.yit.co.il/picserver6/crop_images/2025/04/27/rkDpVeEi1lg/rkDpVeEi1lg_0_2_1284_723_0_large.jpg",
            "publishedAt": "2026-01-16T08:26:51Z",
            "lang": "en",
            "source": {
                "id": "179cfeb8d2cd0b1b229ae6f2db87e23e",
                "name": "Ynetnews",
                "url": "https://www.ynetnews.com"
            }
        },
        {
            "id": "33fa9799362e3046c6a343e0e68d7860",
            "title": "A Synthetic Benchmark Study",
            "description": "Depression is a clinically heterogeneous disorder comprising subtypes such as melancholic, atypical, anxious, and unspecified, each characterized by distinct symptom profiles and treatment responses. Accurate identification of these subtypes is essential for precision psychiatry and optimizing therapeutic outcomes. This study investigates the potential of quantum-inspired feature representations to enhance the classification of depression subtypes from clinical data. A synthetic dataset of 5000 patients was generated, simulating realistic demographic, psychometric, behavioural, and biological variables (e.g., BDI, HRSD, cortisol, sleep, anxiety, BMI). Using standardized and quantum-transformed features, we trained and evaluated four classification models: Random Forest, Support Vector Machine (SVM), XGBoost, and a multi-layer neural network. Quantum-inspired features were derived via parameterized quantum circuits with amplitude encoding and entanglement, yielding fixed-length state vector representations. Performance was assessed using accuracy, weighted F1-score, ROC AUC, and confusion matrices on a held-out test set. Across all models, original clinical features consistently outperformed quantum-transformed features in classification accuracy and subtype separability. Statistical tests confirmed significant performance degradation with quantum features (p < 0.001). Despite this, our framework establishes a reproducible pipeline for benchmarking quantum-inspired machine learning in psychiatry. The findings highlight both the current limitations and future potential of quantum-based representations in modelling complex mental health phenotypes. This work serves as a foundation for integrating emerging quantum learning paradigms with clinically relevant, multi-class psychiatric classification tasks.",
            "content": "1. Introduction\nMajor Depressive Disorder (MDD) is a prevalent and disabling psychiatric condition affecting over 300 million people globally [1]-[7]. Far from being a uniform illness, MDD presents in clinically heterogeneous forms that are categoriz... [34194 chars]",
            "url": "https://www.scirp.org/journal/paperinformation?paperid=148881",
            "image": "https://file.scirp.org/image/oalibj2016030109584288.jpg",
            "publishedAt": "2026-01-16T07:19:44Z",
            "lang": "en",
            "source": {
                "id": "48f641eab3fd1aa330e8db4cb1cfe74e",
                "name": "SCIRP Open Access",
                "url": "https://www.scirp.org"
            }
        },
        {
            "id": "a8358bface0724c6debfbabb7929f0eb",
            "title": "Antimicrobial resistance profiles of non-aureus Staphylococci isolated from farm animals, farm environments and companion animals",
            "description": "Non-aureus staphylococci (NAS), once regarded as less pathogenic than Staphylococcus aureus, are now recognized as emerging opportunistic pathogens in both animals and humans. Species such as S. sciuri, S. chromogenes, and S. xylosus are linked to subclinical mastitis, wound infections, and other animal diseases, posing risks to food safety and public health due to the transfer of resistance genes to S. aureus or other bacterial species. This study investigated the prevalence, antimicrobial resistance, and mecA gene of NAS isolated from four districts of Bangladesh. From 180 samples, NAS were isolated using selective media and identified via the VITEK-2 system. Antimicrobial susceptibility was tested by the disc diffusion method following CLSI guidelines, and the mecA gene was detected by PCR. Among six identified species, S. sciuri (4.44%) and S. chromogenes (2.78%) were predominant. Over half of the isolates (51.85%) were multidrug-resistant, with the highest prevalence in Dhaka, while 18.5% carried the mecA gene. These findings demonstrate that NAS, particularly S. sciuri and S. chromogenes, act as significant reservoirs of multidrug and methicillin resistance. These findings highlight the need to integrate NAS into AMR surveillance and emphasize the importance of antimicrobial stewardship, continuous monitoring, and improved biosecurity to reduce public health risks.",
            "content": "Ebani, V. V. Staphylococci, reptiles, amphibians, and humans: what are their relations? Pathogens 13, 607 (2024).\nCheung, G. Y. C. & Otto, M. Virulence mechanisms of Staphylococcal animal pathogens. Int. J. Mol. Sci. 24, 14587 (2023).\nMichalik, M. et... [14464 chars]",
            "url": "https://www.nature.com/articles/s41598-026-36455-9?error=cookies_not_supported&code=3f2e78a9-4885-4c7d-824e-eae105f9e08c",
            "image": "https://www.nature.com/static/images/favicons/nature/favicon-48x48-b52890008c.png",
            "publishedAt": "2026-01-16T04:43:01Z",
            "lang": "en",
            "source": {
                "id": "7abf0df285fbe93cdccffcc7c4088737",
                "name": "Nature",
                "url": "https://www.nature.com"
            }
        }
    ]
}