Chronic kidney disease (CKD) poses a major public health burden. Diabetes mellitus (DM) is one of the major causes of CKD. In patients with DM, it can be difficult to differentiate diabetic kidney ...disease (DKD) from other causes of glomerular damage; it should not be assumed that all DM patients with decreased eGFR and/or proteinuria have DKD. Renal biopsy is the standard for definitive diagnosis, but other less invasive methods may provide clinical benefit. As previously reported, Raman spectroscopy of CKD patient urine with statistical and chemometric modeling may provide a novel, non-invasive methodology for discriminating between renal pathologies.
Urine samples were collected from renal biopsied and non-biopsied patients presenting with CKD secondary to DM and non-diabetic kidney disease. Samples were analyzed by Raman spectroscopy, baselined with the ISREA algorithm, and subjected to chemometric modeling. Leave-one-out cross-validation was used to assess the predictive capabilities of the model.
This proof-of-concept study consisted of 263 samples, including renal biopsied, non-biopsied diabetic and non-diabetic CKD patients, healthy volunteers, and the Surine™ urinalysis control. Urine samples of DKD patients and those with immune-mediated nephropathy (IMN) were distinguished from one another with 82% sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV). Among urine samples from all biopsied CKD patients, renal neoplasia was identified in urine with 100% sensitivity, specificity, PPV, and NPV, and membranous nephropathy was identified with 66.7% sensitivity, 96.4% specificity, 80.0% PPV, and 93.1% NPV. Finally, DKD was identified among a population of 150 patient urine samples containing biopsy-confirmed DKD, other biopsy-confirmed glomerular pathologies, un-biopsied non-diabetic CKD patients (no DKD), healthy volunteers, and Surine™ with 36.4% sensitivity, 97.8% specificity, 57.1% PPV, and 95.1% NPV. The model was used to screen un-biopsied diabetic CKD patients and identified DKD in more than 8% of this population. IMN in diabetic patients was identified among a similarly sized and diverse population with 83.3% sensitivity, 97.7% specificity, 62.5% PPV, and 99.2% NPV. Finally, IMN in non-diabetic patients was identified with 50.0% sensitivity, 99.4% specificity, 75.0% PPV, and 98.3% NPV.
Raman spectroscopy of urine with chemometric analysis may be able to differentiate between DKD, IMN, and other glomerular diseases. Future work will further characterize CKD stages and glomerular pathology, while assessing and controlling for differences in factors such as comorbidities, disease severity, and other lab parameters.
Abstract
Background
Point-of-care ultrasound (POCUS) can aid geriatricians in caring for complex, older patients. Currently, there is limited literature on POCUS use by geriatricians. We conducted a ...national survey to assess current POCUS use, training desired, and barriers among Geriatrics and Extended Care (“geriatric”) clinics at Veterans Affairs Medical Centers (VAMCs).
Methods
We conducted a prospective observational study of all VAMCs between August 2019 and March 2020 using a web-based survey sent to all VAMC Chiefs of Staff and Chiefs of geriatric clinics.
Results
All Chiefs of Staff (
n
=130) completed the survey (100% response rate). Chiefs of geriatric clinics (“chiefs”) at 76 VAMCs were surveyed and 52 completed the survey (68% response rate). Geriatric clinics were located throughout the United States, mostly at high-complexity, urban VAMCs. Only 15% of chiefs responded that there was some POCUS usage in their geriatric clinic, but more than 60% of chiefs would support the implementation of POCUS use. The most common POCUS applications used in geriatric clinics were the evaluation of the bladder and urinary obstruction. Barriers to POCUS use included a lack of trained providers (56%), ultrasound equipment (50%), and funding for training (35%). Additionally, chiefs reported time utilization, clinical indications, and low patient census as barriers.
Conclusions
POCUS has several potential applications for clinicians caring for geriatric patients. Though only 15% of geriatric clinics at VAMCs currently use POCUS, most geriatric chiefs would support implementing POCUS use as a diagnostic tool. The greatest barriers to POCUS implementation in geriatric clinics were a lack of training and ultrasound equipment. Addressing these barriers systematically can facilitate implementation of POCUS use into practice and permit assessment of the impact of POCUS on geriatric care in the future.
Background
Characterising intra and inter-subject variability of motor-evoked potential (MEP) measurements from transcranial magnetic stimulation (TMS) is key to its development as a diagnostic tool.
...Methods
We performed three experiments to elucidate MEP variability within subjects: (i) repeated measurements at different levels of stimulation and muscle activation, (ii) simultaneous measurements at pairs of ipsilateral and contralateral muscles, (iii) simultaneous measurements of contralateral muscles when one is activated.
Results
Cube root transformation of MEP data produces approximately constant coefficient of variation with Gaussian distribution and no significant autocorrelation between repeat measurements. After adjustment of intersubject variability, correlation between simultaneous muscle pairs was insignificant (
p
= 0.36). Highly significant effects were observed due to increase in intensity of stimulation: (0.07, 0.23) mV,
p
< 0.0001, muscle type: (
p
< 0.009) and activation of ipsilateral muscle: (0.10, 0.49) mV,
p
< 0.0001.
Conclusion
Corticospinal effect is dominated by intersubject variability in simultaneous measurements on normal population.
Hypoxia causes a cascade of activity from the level of the individual down to the regulation and function of the cell nucleus. Prolonged periods of low oxygen tension are a core feature of several ...disease states. Advances in the study of molecular biology have begun to bridge the gap between the cellular response to hypoxia and physiology. Hyperbaric oxygen therapy is a treatment for hypoxic- and inflammatory-driven conditions, in which patients are treated with 100% oxygen at pressures greater than atmospheric pressure. This review discusses hypoxia, the physiologic changes associated with hypoxia, the responses that occur in the cells during hypoxic conditions, and the role that hyperbaric oxygen therapy can play as part of the treatment for many patients suffering from diseases with underlying hypoxia.
Research Summary
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory ...inductive or abductive research, or for post hoc analysis of regression results to detect patterns that may have gone unnoticed. However, ML models should not be treated as the result of a deductive causal test. To demonstrate the application of ML for pattern discovery, we implement ML algorithms to study employee turnover at a large technology company. We interpret the relationships between variables using partial dependence plots, which uncover surprising nonlinear and interdependent patterns between variables that may have gone unnoticed using traditional methods. To guide readers evaluating ML for pattern discovery, we provide guidance for evaluating model performance, highlight human decisions in the process, and warn of common misinterpretation pitfalls. The Supporting Information section provides code and data to implement the algorithms demonstrated in this article.
Managerial Summary
Supervised machine learning (ML) methods are a powerful toolkit that might help managers and researchers discover interesting patterns in large and complex data. We demonstrate this by using several ML algorithms to investigate the drivers of employee turnover at a large technology company. We evaluate the performance of the models, and use visual tools to interpret the patterns revealed. These patterns can be useful in understanding turnover, but we caution not to confuse correlation with causation. These methods should be viewed as “exploratory” and not conclusive proof of relationships in the data. Our guidance can be helpful for managers evaluating analysis conducted by data scientists in their organizations.
Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented worker performance. Reconciling these perspectives, we theorize that intermediate levels of ...domain experience are optimal for algorithm-augmented performance, due to the interplay between two countervailing forces—ability and aversion. Although domain experience can increase performance via increased ability to complement algorithmic advice (e.g., identifying inaccurate predictions), it can also decrease performance via increased aversion to accurate algorithmic advice. Because ability developed through learning by doing increases at a decreasing rate, and algorithmic aversion is more prevalent among experts, we theorize that algorithm-augmented performance will first rise with increasing domain experience, then fall. We test this by exploiting a within-subjects experiment in which corporate information technology support workers were assigned to resolve problems both manually and using an algorithmic tool. We confirm that the difference between performance with the algorithmic tool versus without the tool was characterized by an inverted U-shape over the range of domain experience. Only workers with moderate domain experience did significantly better using the algorithm than resolving tickets manually. These findings highlight that, even if greater domain experience increases workers’ ability to complement algorithms, domain experience can also trigger other mechanisms that overcome the positive ability effect and inhibit performance. Additional analyses and participant interviews suggest that, even though the highest experience workers had the greatest ability to complement the algorithmic tool, they rejected its advice because they felt greater accountability for possible unintended consequences of accepting algorithmic advice.
Recent research has demonstrated that large pre-trained language models reflect societal biases expressed in natural language. The present paper introduces a simple method for probing language models ...to conduct a multilingual study of gender bias towards politicians. We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. To this end, we curate a dataset of 250k politicians worldwide, including their names and gender. Our study is conducted in seven languages across six different language modeling architectures. The results demonstrate that pre-trained language models' stance towards politicians varies strongly across analyzed languages. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians. Finally, and contrary to previous findings, our study suggests that larger language models do not tend to be significantly more gender-biased than smaller ones.
The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. To address this gap, researchers ...and practitioners have encouraged the use of passive technologies. Social media is one such "passive sensor" that has shown potential as a viable "passive sensor" of mental health. However, the construct validity and in-practice reliability of computational assessments of mental health constructs with social media data remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011-2016, and collected 66,000 posts from the university's Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r = 0.86 and SMAPE = 13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students' mental health, particularly their mental health treatment needs.
The electronic and optical properties of monolayer molybdenum disulfide (ML-MoS2) can be manipulated by nonsubstitutional doping with macrocyclic organic metallic molecules like titanyl ...phthalocyanine (TiOPc) or copper phthalocyanine (CuPc). Our density functional theory based calculation reveals a strong noncovalent interaction between metal phthalocyanine (MPc) molecules and ML-MoS2. The adsorption of these molecules results in impurity energy levels in the gap region of ML-MoS2. There exists a considerable charge transfer between MPc molecules and ML-MoS2, which turns ML-MoS2 into a n-type semiconductor. Moreover, both experiment and theoretical results indicate that the adsorption of MPc molecules improves the light absorbance of ML-MoS2 in the entire wavelength range from 400 to 800 nm. Presence of low concentration S vacancy (∼1.67%) in ML-MoS2 has negligible effect on the optical properties and nature of electronic interaction between MPc molecule and ML-MoS2. The implication of the results acquired here has been discussed in term of their applicability in important reactions like hydrogen evolution by water dissociation.
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