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Internal Medicine Internal Medicine

Neutrino intergalactic communication, metal life, and viruses: Part 1 quo vadis ex machina.

In Bioinformation

At one spectrum extreme, Astrobiology conjectures that for exoplanets with Goldilocks conditions, terrestrial-like life is inevitable. Moreover, it is envisaged that via panspermia, terrestrial-like life and its precursors are transferred among galaxies, stars, and within solar systems via transiting comets, asteroids, and planetoids. In addition, expelled stars, which have solar systems, it is inferred, transfer life as well. However, at the other extreme, we propose a paradigm shift that on some planets, subject to non- Goldilocks conditions, metal machine life could arise, ab initio, and evolve viruses, intelligence, and civilizations, conjointly. Accordingly, intelligent mechanized civilizations could readily and efficiently commence space exploration. Furthermore, as a counter paradigm shift, such civilizations could experiment and produce non-metallic life, based on carbon and other non-metal elements, under suitable conditions, related to Goldilocks life. Even a single example of validated interstellar or intergalactic communication received on the Earth would support the existence of life elsewhere. However, the communication platform should not be restricted to electromagnetic radiation. Other platforms should be included as well - one such example, which would require sophisticated technology, is neutrino communication. This is the case for any advanced civilization, be it metal-machine based, biological-based, and carbon-based. In sum, civilizations based on machine life, would be highly productive due to the longevity and hardiness of machine life. However, significant caveats are raised in this brief report, because possibly dissimilar psychologies and intelligence may lead to conflicts between metal machine life and biological life, inter-paradigm conflict.

Shapshak Paul


Astrobiology, Goldilocks, Machine life, ab initio, artificial intelligence, asteroids, comets, conflict, electromagnetic radiation, exoplanets, galaxies, intergalactic and interstellar communication, metals, moons, neutrino, non-metals, origin, panspermia, paradigm, planetoids, planets, psychology, space exploration, stars, virus

Surgery Surgery

A simple and robust method for automating analysis of naïve and regenerating peripheral nerves.

In PloS one ; h5-index 176.0

BACKGROUND : Manual axon histomorphometry (AH) is time- and resource-intensive, which has inspired many attempts at automation. However, there has been little investigation on implementation of automated programs for widespread use. Ideally such a program should be able to perform AH across imaging modalities and nerve states. AxonDeepSeg (ADS) is an open source deep learning program that has previously been validated in electron microscopy. We evaluated the robustness of ADS for peripheral nerve axonal histomorphometry in light micrographs prepared using two different methods.

METHODS : Axon histomorphometry using ADS and manual analysis (gold-standard) was performed on light micrographs of naïve or regenerating rat median nerve cross-sections prepared with either toluidine-resin or osmium-paraffin embedding protocols. The parameters of interest included axon count, axon diameter, myelin thickness, and g-ratio.

RESULTS : Manual and automatic ADS axon counts demonstrated good agreement in naïve nerves and moderate agreement on regenerating nerves. There were small but consistent differences in measured axon diameter, myelin thickness and g-ratio; however, absolute differences were small. Both methods appropriately identified differences between naïve and regenerating nerves. ADS was faster than manual axon analysis.

CONCLUSIONS : Without any algorithm retraining, ADS was able to appropriately identify critical differences between naïve and regenerating nerves and work with different sample preparation methods of peripheral nerve light micrographs. While there were differences between absolute values between manual and ADS, ADS performed consistently and required much less time. ADS is an accessible and robust tool for AH that can provide consistent analysis across protocols and nerve states.

Wong Alison L, Hricz Nicholas, Malapati Harsha, von Guionneau Nicholas, Wong Michael, Harris Thomas, Boudreau Mathieu, Cohen-Adad Julien, Tuffaha Sami


Cardiology Cardiology

Clinical characterization of dysautonomia in long COVID-19 patients.

In Scientific reports ; h5-index 158.0

Increasing numbers of COVID-19 patients, continue to experience symptoms months after recovering from mild cases of COVID-19. Amongst these symptoms, several are related to neurological manifestations, including fatigue, anosmia, hypogeusia, headaches and hypoxia. However, the involvement of the autonomic nervous system, expressed by a dysautonomia, which can aggregate all these neurological symptoms has not been prominently reported. Here, we hypothesize that dysautonomia, could occur in secondary COVID-19 infection, also referred to as "long COVID" infection. 39 participants were included from December 2020 to January 2021 for assessment by the Department of physical medicine to enhance their physical capabilities: 12 participants with COVID-19 diagnosis and fatigue, 15 participants with COVID-19 diagnosis without fatigue and 12 control participants without COVID-19 diagnosis and without fatigue. Heart rate variability (HRV) during a change in position is commonly measured to diagnose autonomic dysregulation. In this cohort, to reflect HRV, parasympathetic/sympathetic balance was estimated using the NOL index, a multiparameter artificial intelligence-driven index calculated from extracted physiological signals by the PMD-200 pain monitoring system. Repeated-measures mixed-models testing group effect were performed to analyze NOL index changes over time between groups. A significant NOL index dissociation over time between long COVID-19 participants with fatigue and control participants was observed (p = 0.046). A trend towards significant NOL index dissociation over time was observed between long COVID-19 participants without fatigue and control participants (p = 0.109). No difference over time was observed between the two groups of long COVID-19 participants (p = 0.904). Long COVID-19 participants with fatigue may exhibit a dysautonomia characterized by dysregulation of the HRV, that is reflected by the NOL index measurements, compared to control participants. Dysautonomia may explain the persistent symptoms observed in long COVID-19 patients, such as fatigue and hypoxia. Trial registration: The study was approved by the Foch IRB: IRB00012437 (Approval Number: 20-12-02) on December 16, 2020.

Barizien Nicolas, Le Guen Morgan, Russel Stéphanie, Touche Pauline, Huang Florent, Vallée Alexandre


Surgery Surgery

Detection of unknown ototoxic adverse drug reactions: an electronic healthcare record-based longitudinal nationwide cohort analysis.

In Scientific reports ; h5-index 158.0

Ototoxic medications can lead to significant morbidity. Thus, pre-marketing clinical trials have assessed new drugs that have ototoxic potential. Nevertheless, several ototoxic side effects of drugs may remain undetected. Hence, we sought to retrospectively investigate the potential risk of ototoxic adverse drug reactions among commonly used drugs via a longitudinal cohort study. An electronic health records-based data analysis with a propensity-matched comparator group was carried out. This study was conducted using the MetaNurse algorithm for standard nursing statements on electronic healthcare records and the National Sample Cohort obtained from the South Korea National Health Insurance Service. Five target drugs capable of causing ototoxic adverse drug reactions were identified using MetaNurse; two drugs were excluded after database-based analysis because of the absence of bilateral hearing loss events in patients. Survival analysis, log-rank test, and Cox proportional hazards regression models were used to calculate the incidence, survival rate, and hazard ratio of bilateral hearing loss among patients who were prescribed candidate ototoxic drugs. The adjusted hazard ratio of bilateral hearing loss was 1.31 (1.03-1.68), 2.20 (1.05-4.60), and 2.26 (1.18-4.33) in cimetidine, hydroxyzine, and sucralfate users, respectively. Our results indicated that hydroxyzine and sucralfate may cause ototoxic adverse drug reactions in patients. Thus, clinicians should consider avoiding co-administration of these drugs with other well-confirmed ototoxic drugs should be emphasized.

Lee Suehyun, Cha Jaehun, Kim Jong-Yeup, Son Gil Myeong, Kim Dong-Kyu


General General

Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples.

In Scientific reports ; h5-index 158.0

Depression is one of the most common mental health issues in the United States, affecting the lives of millions of people suffering from it as well as those close to them. Recent advances in research on mobile sensing technologies and machine learning have suggested that a person's depression can be passively measured by observing patterns in people's mobility behaviors. However, the majority of work in this area has relied on highly homogeneous samples, most frequently college students. In this study, we analyse over 57 million GPS data points to show that the same procedure that leads to high prediction accuracy in a homogeneous student sample (N = 57; AUC = 0.82), leads to accuracies only slightly higher than chance in a U.S.-wide sample that is heterogeneous in its socio-demographic composition as well as mobility patterns (N = 5,262; AUC = 0.57). This pattern holds across three different modelling approaches which consider both linear and non-linear relationships. Further analyses suggest that the prediction accuracy is low across different socio-demographic groups, and that training the models on more homogeneous subsamples does not substantially improve prediction accuracy. Overall, the findings highlight the challenge of applying mobility-based predictions of depression at scale.

Müller Sandrine R, Chen Xi Leslie, Peters Heinrich, Chaintreau Augustin, Matz Sandra C


oncology Oncology

Prediction of individual COVID-19 diagnosis using baseline demographics and lab data.

In Scientific reports ; h5-index 158.0

The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.

Zhang Jimmy, Jun Tomi, Frank Jordi, Nirenberg Sharon, Kovatch Patricia, Huang Kuan-Lin