Bi-allelic variants in the mitochondrial RNase P subunit PRORP cause mitochondrial tRNA processing defects and pleiotropic multisystem presentations.

Human mitochondrial RNase P (mt-RNase P) is responsible for 5' end processing of mitochondrial precursor tRNAs, a vital step in mitochondrial RNA maturation, and is comprised of three protein subunits: TRMT10C, SDR5C1 (HSD10), and PRORP. Pathogenic variants in TRMT10C and SDR5C1 are associated with distinct recessive or x-linked infantile onset disorders, resulting from defects in mitochondrial RNA processing. We report four unrelated families with multisystem disease associated with bi-allelic variants in PRORP, the metallonuclease subunit of mt-RNase P. Affected individuals presented with variable phenotypes comprising sensorineural hearing loss, primary ovarian insufficiency, developmental delay, and brain white matter changes. Fibroblasts from affected individuals in two families demonstrated decreased steady state levels of PRORP, an accumulation of unprocessed mitochondrial transcripts, and decreased steady state levels of mitochondrial-encoded proteins, which were rescued by introduction of the wild-type PRORP cDNA. In mt-tRNA processing assays performed with recombinant mt-RNase P proteins, the disease-associated variants resulted in diminished mitochondrial tRNA processing. Identification of disease-causing variants in PRORP indicates that pathogenic variants in all three subunits of mt-RNase P can cause mitochondrial dysfunction, each with distinct pleiotropic clinical presentations.

Electroencephalography functional connectivity-A biomarker for painful polyneuropathy.

BACKGROUND AND PURPOSE: Advanced analysis of electroencephalography (EEG) data has become an essential tool in brain research. Based solely on resting state EEG signals, a data-driven, predictive and explanatory approach is presented to discriminate painful from non-painful diabetic polyneuropathy (DPN) patients. METHODS: Three minutes long, 64 electrode resting-state recordings were obtained from 180 DPN patients. The analysis consisted of a mixture of traditional, explanatory and machine learning analyses. First, the 10 functional bivariate connections best differentiating between painful and non-painful patients in each EEG band were identified and the relevant receiver operating characteristic was calculated. Later, those connections were correlated with selected clinical parameters. RESULTS: Predictive analysis indicated that theta and beta bands contain most of the information required for discrimination between painful and non-painful polyneuropathy patients, with area under the receiver operating characteristic curve values of 0.93 for theta and 0.89 for beta bands. Assessing statistical differences between the average magnitude of functional connectivity values and clinical pain parameters revealed that painful DPN patients had significantly higher cortical functional connectivity than non-painful ones (p = 0.008 for theta and p = 0.001 for alpha bands). Moreover, intra-band analysis of individual significant functional connections revealed a positive correlation with average reported pain in the previous 3 months in all frequency bands. CONCLUSIONS: Resting state EEG functional connectivity can serve as a highly accurate biomarker for the presence or absence of pain in DPN patients. This highlights the importance of the brain, in addition to the peripheral lesions, in generating the clinical pain picture. This tool can probably be extended to other pain syndromes.

A community-of-practice-based evaluation methodology for knowledge intensive computational methods and its application to multimorbidity decision support.

OBJECTIVE: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans. MATERIALS AND METHODS: Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods-research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods. RESULTS: Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support. Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods. DISCUSSION: The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS. CONCLUSION: We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).

Outcomes of pituitary apoplexy: a comparison of microadenomas and macroadenomas.

PURPOSE: We aimed to assess clinical characteristics of apoplexy of pituitary microadenomas compared to macroadenomas. METHODS: We retrieved clinical records of patients > 18 years old, hospitalized in Rambam hospital between January 2001 and October 2017, with pituitary apoplexy and follow-up of at least one year. We compared clinical course and outcomes of apoplexy between patients with microadenomas and macroadenomas, and between patients who received conservative or surgical treatment. Statistical analysis was done using Fisher's exact and Mann-Whitney tests. RESULTS: Twenty-seven patients with pituitary apoplexy were included in the final analysis: mean age was 40.7 ± 12.5 years, 13(48%) were female, 7(26%) had microadenomas, and 21(78%) harbored clinically nonfunctioning pituitary adenomas. Upon admission, hyponatremia, random cortisol level of < 200 nmol/L, and secondary hypothyroidism, were evident in 6/20, 8/18, and 4/18 patients with macroadenoma and 1/5, 2/5, and 1/6 patients with microadenoma, respectively (P = 1.0). Hypogonadotropic hypogonadism was evident in 9/12 men with macroadenoma. In 12 macroadenoma patients, the tumor abutted the optic chiasm, of whom eight had visual field defects. Fifteen patients with macroadenoma and two with microadenoma underwent transsphenoidal surgery. Median follow-up was 3 years. At last follow-up visit, patients with microadenoma had lower rates of corticotropic deficiency or secondary hypothyroidism compared to macroadenoma patients (1/7 vs. 13/20 respectively, p = 0.033). Only two patients with macroadenomas had persistent visual field defects. Outcomes were comparable between conservative and surgical treatment groups. CONCLUSIONS: Long term pituitary hormone deficiencies are more common in pituitary apoplexy patients with macroadenomas. Apoplexy of pituitary microadenoma carries a more favorable prognosis.

Towards a goal-oriented methodology for clinical-guideline-based management recommendations for patients with multimorbidity: GoCom and its preliminary evaluation.

Patients with chronic multimorbidity are becoming more common as life expectancy increases, making it necessary for physicians to develop complex management plans. We are looking at the patient management process as a goal-attainment problem. Hence, our aim is to develop a goal-oriented methodology for providing decision support for managing patients with multimorbidity continuously, as the patient's health state is progressing and new goals arise (e.g., treat ulcer, prevent osteoporosis). Our methodology allows us to detect and mitigate inconsistencies among guideline recommendations stemming from multiple clinical guidelines, while consulting medical ontologies and terminologies and relying on patient information standards. This methodology and its implementation as a decision-support system, called GoCom, starts with computer-interpretable clinical guidelines (CIGs) for single problems that are formalized using the PROforma CIG language. We previously published the architecture of the system as well as a CIG elicitation guide for enriching PROforma tasks with properties referring to vocabulary codes of goals and physiological effects of management plans. In this paper, we provide a formalization of the conceptual model of GoCom that generates, for each morbidity of the patient, a patient-specific goal tree that results from the PROforma engine's enactment of the CIG with the patient's data. We also present the "Controller" algorithm that drives the GoCom system. Given a new problem that a patient develops, the Controller detects inconsistencies among goals pertaining to different comorbid problems and consults the CIGs to generate alternative non-conflicted and goal-oriented management plans that address the multiple goals simultaneously. In this stage of our research, the inconsistencies that can be detected are of two types - starting vs. stopping medications that belong to the same medication class hierarchy, and detecting opposing physiological effect goals that are specified in concurrent CIGs (e.g., decreased blood pressure vs. increased blood pressure). However, the design of GoCom is modular and generic and allows the future introduction of additional interaction detection and mitigation strategies. Moreover, GoCom generates explanations of the alternative non-conflicted management plans, based on recommendations stemming from the clinical guidelines and reasoning patterns. GoCom's functionality was evaluated using three cases of multimorbidity interactions that were checked by our three clinicians. Usefulness was evaluated with two studies. The first evaluation was a pilot study with ten 6th year medical students and the second evaluation was done with 27 6th medical students and interns. The participants solved complex realistic cases of multimorbidity patients: with and without decision-support, two cases in the first evaluation and 6 cases in the second evaluation. Use of GoCom increased completeness of the patient management plans produced by the medical students from 0.44 to 0.71 (P-value of 0.0005) in the first evaluation, and from 0.31 to 0.78 (P-value < 0.0001) in the second evaluation. Correctness in the first evaluation was very high with (0.98) or without the system (0.91), with non-significant difference (P-value ≥ 0.17). In the second evaluation, use of GoCom increased correctness from 0.68 to 0.83 (P-value of 0.001). In addition, GoCom's explanation and visualization were perceived as useful by the vast majority of participants. While GoCom's detection of goal interactions is currently limited to detection of starting vs. stopping the same medication or medication subclasses and detecting conflicting physiological effects of concurrent medications, the evaluation demonstrated potential of the system for improving clinical decision-making for multimorbidity patients.

Real-World Evidence on the Effect of Missing an Oral Contraceptive Dose: Analysis of Internet Search Engine Queries.

BACKGROUND: Oral contraceptives (OCs) are a unique chronic medication with which a memory slip may result in a threat that could change a person's life course. Subjective concerns of missed OC doses among women have been addressed infrequently. Anonymized queries to internet search engines provide unique access to concerns and information gaps faced by a large number of internet users. OBJECTIVE: We aimed to quantitate the frequency of queries by women seeking information in an internet search engine, after missing one or more doses of an OC; their further queries on emergency contraception, abortion, and miscarriage; and their rate of reporting a pregnancy timed to the cycle of missing an OC. METHODS: We extracted all English-language queries submitted to Bing in the United States during 2018, which mentioned a missed OC and subsequent queries of the same users on miscarriage, abortion, emergency contraceptives, and week of pregnancy. RESULTS: We identified 26,395 Bing users in the United States who queried about missing OC pills and the fraction that further queried about miscarriage, abortion, emergency contraceptive, and week of pregnancy. Users under the age of 30 years who asked about forgetting an OC dose were more likely to ask about abortion (1.5 times) and emergency contraception (1.7 times) (P<.001 for both), while users at ages of 30-34 years were more likely to query about pregnancy (2.1 times) and miscarriage (5.4 times) (P<.001 for both). CONCLUSIONS: Our data indicate that many women missing a dose of OC might not have received sufficient information from their health care providers or chose to obtain it online. Queries about abortion and miscarriage peaking in the subsequent days indicate a common worry of possible pregnancy. These results reinforce the importance of providing comprehensive written information on missed pills when prescribing an OC.

Assessment of the Frequency of Online Searches for Symptoms Before Diagnosis: Analysis of Archival Data.

BACKGROUND: Surveys suggest that a large proportion of people use the internet to search for information on medical symptoms they experience and that around one-third of the people in the United States self-diagnose using online information. However, surveys are known to be biased, and the true rates at which people search for information on their medical symptoms before receiving a formal medical diagnosis are unknown. OBJECTIVE: This study aimed to estimate the rate at which people search for information on their medical symptoms before receiving a formal medical diagnosis by a health professional. METHODS: We collected queries made on a general-purpose internet search engine by people in the United States who self-identified their diagnosis from 1 of 20 medical conditions. We focused on conditions that have evident symptoms and are neither screened systematically nor a part of usual medical care. Thus, they are generally diagnosed after the investigation of specific symptoms. We evaluated how many of these people queried for symptoms associated with their medical condition before their formal diagnosis. In addition, we used a survey questionnaire to assess the familiarity of laypeople with the symptoms associated with these conditions. RESULTS: On average, 15.49% (1792/12,367, SD 8.4%) of people queried about symptoms associated with their medical condition before receiving a medical diagnosis. A longer duration between the first query for a symptom and the corresponding diagnosis was correlated with an increased likelihood of people querying about those symptoms (rho=0.6; P=.005); similarly, unfamiliarity with the association between a condition and its symptom was correlated with an increased likelihood of people querying about those symptoms (rho=-0.47; P=.08). In addition, worrying symptoms were 14% more likely to be queried about. CONCLUSIONS: Our results indicate that there is large variability in the percentage of people who query the internet for their symptoms before a formal medical diagnosis is made. This finding has important implications for systems that attempt to screen for medical conditions.

Management of cystic prolactinomas: a review.

INTRODUCTION: Cystic prolactinoma is a variant of prolactin-secreting pituitary adenoma. The strategies for the management of cystic prolactinoma have not been addressed thoroughly in clinical guidelines. METHODS: A literature search was performed using Pubmed to review the current approaches to the treatment of cystic prolactinoma. RESULTS: Transsphenoidal resection is an effective and relatively safe approach for the treatment of cystic prolactinoma, however, morbidity of surgery is dependent on the skill of the surgeon. Emerging studies allude to the efficacy and safety of dopamine agonists in the management of cystic prolactinoma. Dopamine agonists are associated with considerable rates of clinical improvement and tumor shrinkage, hence reducing the need for surgical intervention. CONCLUSIONS: Recent studies suggest that dopamine agonist therapy may be an effective and safe treatment option in a considerable portion of patients with cystic prolactinomas. We suggest that dopamine agonists should be considered as a first-line therapy for cystic prolactinoma in the absence of indications for early surgical intervention.

Interaction Between the Haptoglobin Genotype and Vitamin E on Cardiovascular Disease in Diabetes.

PURPOSE OF REVIEW: Despite compelling evidence regarding the importance of oxidant stress in the development of vascular complications and observational studies suggesting that vitamin E may be protective from these complications, multiple clinical trials have failed to show benefit from vitamin E supplementation in the prevention of vascular complications in diabetes. One possible explanation for this failure of vitamin E may have been inappropriate patient selection. This review seeks to provide the clinical evidence and mechanistic basis for why a subset of individuals defined by their haptoglobin (Hp) genotype may derive cardiovascular protection by vitamin E supplementation. RECENT FINDINGS: Clinical trial data from the HOPE, ICARE, and WHS studies is presented showing a pharmacogenomic interaction between the Hp genotype and vitamin E on the development of CVD. Specifically, in individuals with diabetes and the Hp2-2 genotype, vitamin E has been shown to be associated with an approximately 35% reduction in CVD. Cardioprotection by vitamin E in individuals with the Hp2-2 genotype appears to be mediated in part by an improvement in HDL functionality as demonstrated in three independent trials in both type 1 diabetes and type 2 diabetes. Vitamin E may provide benefit in reducing CVD in Hp2-2 individuals with diabetes. However, in order for this pharmacogenomic algorithm to be accepted as a standard of care and used clinically, an additional large prospective study will need to be performed.

Encouraging Physical Activity in Patients With Diabetes: Intervention Using a Reinforcement Learning System.

BACKGROUND: Regular physical activity is known to be beneficial for people with type 2 diabetes. Nevertheless, most of the people who have diabetes lead a sedentary lifestyle. Smartphones create new possibilities for helping people to adhere to their physical activity goals through continuous monitoring and communication, coupled with personalized feedback. OBJECTIVE: The aim of this study was to help type 2 diabetes patients increase the level of their physical activity. METHODS: We provided 27 sedentary type 2 diabetes patients with a smartphone-based pedometer and a personal plan for physical activity. Patients were sent short message service messages to encourage physical activity between once a day and once per week. Messages were personalized through a Reinforcement Learning algorithm so as to improve each participant's compliance with the activity regimen. The algorithm was compared with a static policy for sending messages and weekly reminders. RESULTS: Our results show that participants who received messages generated by the learning algorithm increased the amount of activity and pace of walking, whereas the control group patients did not. Patients assigned to the learning algorithm group experienced a superior reduction in blood glucose levels (glycated hemoglobin [HbA1c]) compared with control policies, and longer participation caused greater reductions in blood glucose levels. The learning algorithm improved gradually in predicting which messages would lead participants to exercise. CONCLUSIONS: Mobile phone apps coupled with a learning algorithm can improve adherence to exercise in diabetic patients. This algorithm can be used in large populations of diabetic patients to improve health and glycemic control. Our results can be expanded to other areas where computer-led health coaching of humans may have a positive impact. Summary of a part of this manuscript has been previously published as a letter in Diabetes Care, 2016.

Design of a bi-directional methodology for automated assessment of compliance to continuous application of clinical guidelines, and its evaluation in the type 2 diabetes domain.

We introduce a new approach for automated guideline-based-care quality assessment, the bidirectional knowledge-based assessment of compliance (BiKBAC) method, and the DiscovErr system, which implements it. Our methodology compares the guideline's Asbru-based formal representation, including its intentions, with the longitudinal medical record, using a top-down and bottom-up approach. Partial matches are resolved using fuzzy temporal logic. The system was evaluated in the type 2 Diabetes management domain, comparing it to three expert clinicians, including two diabetes experts. The system and the experts commented on the management of 10 patients, randomly selected from 2,000 diabetes patients. On average, each record spanned 5.23 years; the data included 1,584 medical transactions. The system provided 279 comments. The experts made 181 different unique comments. The completeness (recall) of the system was 91% when the gold standard was comments made by at least two of the three experts, and 98%, compared to comments made by all three experts. The experts also assessed all of the 114 medication-therapy-related comments, and a random 35% of the 165 tests-and-monitoring-related comments. The system's correctness (precision) was 81%, compared to comments judged as correct by both diabetes experts, and 91%, compared to comments judged as correct by one diabetes expert and at least as partially correct by the other. 89% of the comments were judged as important by both diabetes experts, 8% were judged as important by one expert, and 3% were judged as less important by both experts. Adding the validated system comments to the experts' comments, the completeness scores of the experts were 75%, 60%, and 55%; the expert correctness scores were respectively 99%, 91%, and 88%. Thus, the system could be ranked first in completeness and second in correctness. We conclude that systems such as DiscovErr can effectively assess the quality of continuous guideline-based care.

A Large-Scale Observational Analysis of Social Media Data Reveals Major Public Misperception of the Attainability of Drastic Weight Loss by Dieting.

INTRODUCTION: Diet forums in social media websites provide an opportunity to glimpse the experience of different weight loss diet strategies reported by tens of thousands of individuals. METHODS: We analyzed all postings with weight information from the six major Reddit weight loss diet forums ("subreddits") as reported by forum participants. RESULTS: Data were collected from January 2011 to April 2020 from all 55,900 users posting weight information. Average start BMI was in the overweight or obese range (26-34 kg/m2), and average goal BMI was in the normal range (21.5-24.5 kg/m2) for all subreddits. There is correlation between start BMI and goal BMI (R2 = 0.63, p < 10-10) and between planned weight loss and reported weight loss (R2 = 0.56, p < 10-10). Approximately 80% of forum participants reported a weight loss that was greater than 5% of their initial body weight. Actual reported weight loss was less than half of goal weight loss. Average reported weight loss and adherence were highest in the keto and loseit subreddits. More upvotes and fewer downvotes were associated with higher reported weight loss in five of the six subreddits. CONCLUSIONS: Despite the need for cautious interpretation of these data due to self-selection of users who updated weight loss and the possibility of unreliable weight reports, the study has several findings. Average goal BMI was in the normal weight range, demonstrating a highly unrealistic perception, in a very large lay-public cohort, of the plausibility of losing all excess weight. The success in weight loss and maintenance in self-selected individuals who continued reporting weight for many months may demonstrate the subjective value some individuals can obtain from forum participation.

Do patients read emails from their physician containing tips on improving lifestyle habits? A pilot study.

INTRODUCTION: Healthcare providers and organizations occasionally use electronic messages to provide information to patients. There is insufficient data on whether patients actually read the emails they receive. In this study, we aimed to assess the cooperation of patients in reading multiple information pages sent over 6 months from their diabetologist via email. METHODS: Adults with non-optimally controlled type 2 diabetes received via email, once every 2 weeks for 6 months, a message containing information and tips on how to improve diabetes control through lifestyle choices. The information was provided in a format that required the recipient to actively click on a "read more" tab in order to reveal the entire text. Each email contained a short questionnaire requesting a response. Analysis compared the effect of patient variables on co-operation with reading the emails and answering the questionnaires. MAIN FINDINGS: 45 patients completed the study, 53.3% of them read 66-100% of the emails, 17.8% read 34-65% of the emails and only 26.7% read less than 33% of the emails. Women answered more questionnaires than men did. Answering a questionnaire on nutrition or medications correlated with reading the following email sent. CONCLUSIONS: This study is the first to demonstrate that most patients do indeed read a significant portion of emails sent by their physician. Email could be an effective means of sharing information and improving patient engagement with treatment.

Towards a framework for comparing functionalities of multimorbidity clinical decision support: A literature-based feature set and benchmark cases.

Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present (1) a set of features for MGCDS, which were derived using a literature review and evaluated by physicians using a survey, and (2) a set of benchmarking case studies, which illustrate the clinical application of these features. This work represents the first necessary step in a broader research program aimed at the development of a benchmark framework that allows for standardized and comparable MGCDS evaluations, which will facilitate the assessment of functionalities of MGCDS, as well as highlight important gaps in the state-of-the-art. We also outline our future work on developing the framework, specifically, (3) a standard for reporting MGCDS solutions for the benchmark case studies, and (4) criteria for evaluating these MGCDS solutions. We plan to conduct a large-scale comparison study of existing MGCDS based on the comparative framework.

Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies.

BACKGROUND: Privacy restrictions limit access to protected patient-derived health information for research purposes. Consequently, data anonymization is required to allow researchers data access for initial analysis before granting institutional review board approval. A system installed and activated at our institution enables synthetic data generation that mimics data from real electronic medical records, wherein only fictitious patients are listed. OBJECTIVE: This paper aimed to validate the results obtained when analyzing synthetic structured data for medical research. A comprehensive validation process concerning meaningful clinical questions and various types of data was conducted to assess the accuracy and precision of statistical estimates derived from synthetic patient data. METHODS: A cross-hospital project was conducted to validate results obtained from synthetic data produced for five contemporary studies on various topics. For each study, results derived from synthetic data were compared with those based on real data. In addition, repeatedly generated synthetic datasets were used to estimate the bias and stability of results obtained from synthetic data. RESULTS: This study demonstrated that results derived from synthetic data were predictive of results from real data. When the number of patients was large relative to the number of variables used, highly accurate and strongly consistent results were observed between synthetic and real data. For studies based on smaller populations that accounted for confounders and modifiers by multivariate models, predictions were of moderate accuracy, yet clear trends were correctly observed. CONCLUSIONS: The use of synthetic structured data provides a close estimate to real data results and is thus a powerful tool in shaping research hypotheses and accessing estimated analyses, without risking patient privacy. Synthetic data enable broad access to data (eg, for out-of-organization researchers), and rapid, safe, and repeatable analysis of data in hospitals or other health organizations where patient privacy is a primary value.

Evolutionary Perspective in Rickets and Vitamin D.

Modern lifestyle limits our exposure to sunlight, which photosynthesizes vitamin D in the skin, and the incidence of nutritional rickets has been resurging. Vitamin D is one of the first hormones; it is photosynthesized in all organism from the phytoplankton to mammals. A selective sweep of the promoter of the vitamin D receptor (VDR) happened as soon as Homo sapiens migrated out of Africa; it co-adapted with skin color genes to provide adaptation to latitudes and the levels of exposure to ultraviolet (UV)B radiation along the route out of Africa. Exposure to UVB radiation balances the need for vitamin D photosynthesis and degradation of folic acid by UVB radiation. Skin color follows a latitude distribution: the darkest populations dwell in the tropical belt; and the fair-skinned populations inhabit the northern countries. Due to their greater need for calcium during their reproductive life, the skin color of women is lighter- than that of men. Vitamin D is essential for mineral homeostasis and has a wide variety of non-skeletal functions, of which the most important for natural selection is a regulatory function in the innate immune system. In the human fossil record, vitamin D deficiency coincided with bone tuberculosis. About 6,000 years ago, a diet which included cow's milk provided Neolithic humans with twice as much calcium and was more alkaline than that of its Paleolithic predecessors. Adiposity is negatively associated with the vitamin D status and obese individuals require 2-3 times more vitamin D than non-obese individuals to normalize circulating 25OHD levels. In an era of an obesity epidemic, we need more research to determine whether adiposity should be considered when determining the dietary requirements for vitamin D and calcium and the optimal serum 25OHD levels.

Can internet search engine queries be used to diagnose diabetes? Analysis of archival search data.

AIMS: Diabetes is often diagnosed late. This study aimed to assess the possibility for earlier detection of diabetes from search data, using predictive models trained on large-scale data. METHODS: We extracted all English-language queries made by people in the USA to Bing during 1 year and identified queries containing symptoms of diabetes. We compared the ability of four different prediction models (linear regression, logistic regression, decision tree and random forest) to distinguish between users who stated that they were diagnosed with diabetes and users who did not refer to diabetes or diabetes drugs but queried about at least one of the symptoms. RESULTS: We identified 11,050 "new diabetes users" who stated they had been diagnosed with diabetes and approximately 11.5 million "control users" who queried about symptoms without querying for terms related to diabetes. Both the logistic regression and the random forest models were able to distinguish between the populations with an area under curve of 0.92 which translates to a positive predictive value of 56% at a false-positive rate of 1%. The model could identify patients up to 240 days before they mentioned being diagnosed. CONCLUSIONS: Some undiagnosed diabetes patients can be detected accurately according to their symptom queries to a search engine. Such earlier diagnosis, especially in cases of type 1 diabetes, could be clinically meaningful. The ability of search engines to serve as a population-wide screening tool could potentially be improved using additional data provided by users.

Antihyperglycemic therapy during colonoscopy preparation: A review and suggestions for practical recommendations.

Patients with diabetes have distinct risks during precolonoscopy bowel preparation, caused by change of diet, change of antihyperglycemic medication regimens and diabetes-related complications and comorbidities. These risks include hypoglycemia, water and electrolyte imbalance, acute renal failure, lactic acidosis, ketoacidosis and low quality of bowel preparation. Here we review the existing literature and present recommendations from a subcommittee of the Israeli National Diabetes Council for primary care physicians and gastroenterologists. The instructions include general suggestions on diet and glucose monitoring and specific recommendations on timing of modification or cessation of each of the antihyperglycemic medication types to ensure patient safety during bowel preparation.

Managing Diabetes in Patients Hospitalized in Internal Medicine Units.

Diabetes and hyperglycemia are present in over one-third of inpatients in internal medicine units and are associated with worse prognosis in multiple morbidities. Treatment of inpatient hyperglycemia is usually with basal bolus insulin in a dose calculated by the patient's weight, with lower doses recommended in patients who are at a higher risk for hypoglycemia. Other antihyperglycemic medications and insulin regimens can be used in selected patients. There are no adequately powered studies on the effect of improving glycemic control on hospitalization outcomes in non-critically ill patients in internal medicine units, and in most patients a modest glucose target of 140-180 mg/dL is recommended. A structured discharge plan should intensify antihyperglycemic treatment as needed and include an outpatient follow-up appointment shortly after discharge.

A Young Patient with Leg Weakness and Hypokalemia-Case Report.

A 20-year-old female patient was admitted to hospital because of bilateral leg weakness. Laboratory investigation showed metabolic alkalosis and severe hypokalemia. Differential diagnosis included mineralocorticoid or apparent mineralocorticoid excess diseases, with a high aldosterone-to-renin ratio (ARR) after correcting hypokalemia. After confirmatory tests, imaging studies revealed a unilateral adrenocortical adenoma consistent with Conn's disease. Surgery was curative.