Abstract:Two of the biggest challenges in medicine today are the need to detect diseases in a non-invasive manner, and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularlymodified Silicon Nanowire Field Effect Transistors (SiNW FETs) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma and Chronic Obstructive Pulmonary Disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlated their sensitivity and selectivity towards volatile organic compounds (VOCs) linked with diseased states. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples showed that the optimized SiNW FETs can detectand discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct positive way, which can reassure patients and prevent numerous negative investigations. 3Physicians are always challenged by the need to give the correct diagnosis as early in the onset of a disease is possible, whether the disease-related symptoms are absent or not evident.1 Symptoms are not always characteristic of one particular disease; overlap of symptoms is common in, for example, lung diseases. 2 Patients with different respiratory diseases, such as malignant or benign tumors, or substantially less severe diseases, may have similar symptoms, e.g. cough, chest pain, difficulty to breathe, etc. These symptoms may be characteristic of lung cancer (LC), pneumonia, asthma, and chronic obstructive pulmonary disease (COPD). 1,2Therefore, it is of particular clinical importance to find a diagnostic tool capable of distinguishing between these diseases. A diagnostic tool that involves no needle, surgery and/or active materials and/or radioactive exposure would have a benefit.A highly promising approach that could meet the aforementioned need is based on the detection and classification of the disease breathprint, viz. the chemical profiles of highly-and semi-VOCs in exhaled breath linked with disease. [3][4][5][6][7][8][9][10][11][12][13][14][15] The rationale behind this approach relies on the fact that VOCs generated by cellular metabolic pathways during a specific disease circulate in the blood stream and diffuse into exhaled breath, which is easily sampled. 4,16,17 In certain instances, analysis of breathprints offers several potential advantages, such as: (a) breath samples are non-invasive and easy to obtain; (b) breath contains less complicated mixtures than either serum or urine; and (c) breath testing has the potential for direct and real-time diagnosis and monitoring. 3,18-21Several mass-spectrometry and spectroscopy studies have shown that the breathprint of a specific disease differs from that of healthy control...

Breath analysis could discriminate patients with LC who harbor the EGFR mutation from those with wild-type EGFR and those with benign pulmonary nodules from those patients with early LC. A positive breath print for the EGFR mutation may be used in treatment decisions if tissue sampling does not provide adequate material for definitive mutation analysis.

Breath analysis, using mainly the nanoarray, may serve as a surrogate marker for the response to systemic therapy in lung cancer. As a monitoring tool, it can provide the oncologist with a quick bedside method of identifying a lack of response to an anticancer treatment. This may allow quicker recognition than does the current RECIST analysis. Early recognition of treatment failure could improve patient care.

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T he echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) gene fusion occurs in 2% to 7% of non-small-cell lung cancer cases. 1 Tumors expressing this fusion respond to treatment with crizotinib, an ALK tyrosine kinase inhibitor. However, brain metastases frequently occur, even in the presence of systemic response to therapy. 2

Aims To assess the validity of the diagnostic codes relating to diabetic foot ulcer (DFU) in the electronic medical records of a large integrated care provider and to assess the prevalence of DFU among its members. Materials and Methods Data were obtained from the diabetes registry of Maccabi Healthcare Services (MHS), a 2.1‐million‐member sick‐fund in Israel, which included 125 665 patients in 2015. We randomly selected and reviewed ~400 patient files from each of the following categories during study period: (1) had a diagnostic code of DFU; (2) had a diagnostic code, or clinical condition suggestive of DFU including: leg‐ulcer, amputation, DFU in quartiles proximate to 2015 or abnormality reported by nurse; (3) patients at high risk for DFU (age > 35 and one of the following: peripheral artery disease, neuropathy, DFU during 2011‐2014, eGFR<30 mL/min/m2 or foot deformity). The patients' charts were reviewed by study physicians, and DFU was validated or refuted. Results Relying upon diagnostic codes entered by physicians, the positive predictive value (PPV) was 73.1% (95% CI 67.6‐78.2), and the sensitivity was 48.2% (95% CI 45.8‐50.7%). The PPV of the diagnostic codes listed by podiatrists were significantly lower, while that of codes listed by nurses was higher but with lower sensitivity. The estimated annual prevalence of DFU in the diabetes registry of MHS was 1.2% (95%CI 1.0‐1.5%). Conclusions Diagnostic codes alone cannot be used reliably to create a DFU registry. Nevertheless, the data collected provide an estimate of the prevalence of DFU among patients included in the MHS diabetes registry.

Background: Risk stratification models have been developed to identify patients that are at a higher risk of COVID-19 infection and severe illness. Objectives To develop and implement a scoring tool to identify COVID-19 patients that are at risk for severe illness during the Omicron wave. Methods: This is a retrospective cohort study that was conducted in Israel’s second-largest healthcare maintenance organization. All patients with a new episode of COVID-19 between 26 November 2021 and 18 January 2022 were included. A model was developed to predict severe illness (COVID-19-related hospitalization or death) based on one-third of the study population (the train group). The model was then applied to the remaining two-thirds of the study population (the test group). Risk score sensitivity, specificity, and positive predictive value rates, and receiver operating characteristics (ROC) were calculated to describe the performance of the model. Results: A total of 409,693 patients were diagnosed with COVID-19 over the two-month study period, of which 0.4% had severe illness. Factors that were associated with severe disease were age (age > 75, OR-70.4, 95% confidence interval [CI] 42.8–115.9), immunosuppression (OR-4.8, 95% CI 3.4–6.7), and pregnancy (5 months or more, OR-82.9, 95% CI 53–129.6). Factors that were associated with a reduced risk for severe disease were vaccination status (patients vaccinated in the previous six months OR-0.6, 95% CI 0.4–0.8) and a prior episode of COVID-19 (OR-0.3, 95% CI 0.2–0.5). According to the model, patients who were in the 10th percentile of the risk severity score were considered at an increased risk for severe disease. The model accuracy was 88.7%. Conclusions: This model has allowed us to prioritize patients requiring closer follow-up by their physicians and outreach services, as well as identify those that are most likely to benefit from anti-viral treatment during the fifth wave of infection in Israel, dominated by the Omicron variant.

A 24-hour Holter electrocardiogram (ECG) is frequently employed to detect occult AF following ischaemic CVA or TIA. Real-world data demonstrates detection rates of 1.3% using this method. 24-hour Holter monitoring serves as an initial screening tool, yet a more efficient method for prolonged monitoring should be applied.