Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria.

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the "chemical family type" attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.

OBJECTIVE: The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital. METHODS: The CKD risk model estimates a patient's risk of developing CKD 3 to 12 months following an inpatient admission. The model was developed on a retrospective dataset of 4,879 admissions from 2014 to 2018, then run silently on 1,270 admissions from April to October, 2019. Three metrics were used to monitor its performance during the silent phase: (1) standardized mean differences (SMDs); (2) performance of a "membership model"; and (3) response distribution analysis. Observed patient outcomes for the 1,270 admissions were used to calculate prospective model performance and the ability of the three metrics to detect performance changes. RESULTS: The deployed model had an area under the receiver-operator curve (AUROC) of 0.63 in the prospective evaluation, which was a significant decrease from an AUROC of 0.76 on retrospective data (p = 0.033). Among the three metrics, SMDs were significantly different for 66/75 (88%) of the model's input variables (p <0.05) between retrospective and deployment data. The membership model was able to discriminate between the two settings (AUROC = 0.71, p <0.0001) and the response distributions were significantly different (p <0.0001) for the two settings. CONCLUSION: This study suggests that the three metrics examined could provide early indication of performance deterioration in deployed models' performance.

Clinical decision support in the neonatal ICU.

Clinical Decision Support (CDS) tools help the healthcare team diagnose, monitor, and treat patients more efficiently and consistently by executing clinical practice guidelines and recommendations. As a result, CDS has a direct impact on the delivery and healthcare outcomes. This review covers the fundamental concepts, as well as the infrastructure needed to create a CDS tool and examples of its use in the neonatal setting. This article also serves as a primer on what to think about when proposing the development of a new CDS tool, or when upgrading an existing one. We also highlight important elements that influence CDS development, such as informatics methodologies, data and device interoperability, and regulation.

A Data-Driven Algorithm to Recommend Initial Clinical Workup for Outpatient Specialty Referral: Algorithm Development and Validation Using Electronic Health Record Data and Expert Surveys.

BACKGROUND: Millions of people have limited access to specialty care. The problem is exacerbated by ineffective specialty visits due to incomplete prereferral workup, leading to delays in diagnosis and treatment. Existing processes to guide prereferral diagnostic workup are labor-intensive (ie, building a consensus guideline between primary care doctors and specialists) and require the availability of the specialists (ie, electronic consultation). OBJECTIVE: Using pediatric endocrinology as an example, we develop a recommender algorithm to anticipate patients' initial workup needs at the time of specialty referral and compare it to a reference benchmark using the most common workup orders. We also evaluate the clinical appropriateness of the algorithm recommendations. METHODS: Electronic health record data were extracted from 3424 pediatric patients with new outpatient endocrinology referrals at an academic institution from 2015 to 2020. Using item co-occurrence statistics, we predicted the initial workup orders that would be entered by specialists and assessed the recommender's performance in a holdout data set based on what the specialists actually ordered. We surveyed endocrinologists to assess the clinical appropriateness of the predicted orders and to understand the initial workup process. RESULTS: Specialists (n=12) indicated that <50% of new patient referrals arrive with complete initial workup for common referral reasons. The algorithm achieved an area under the receiver operating characteristic curve of 0.95 (95% CI 0.95-0.96). Compared to a reference benchmark using the most common orders, precision and recall improved from 37% to 48% (P<.001) and from 27% to 39% (P<.001) for the top 4 recommendations, respectively. The top 4 recommendations generated for common referral conditions (abnormal thyroid studies, obesity, amenorrhea) were considered clinically appropriate the majority of the time by specialists surveyed and practice guidelines reviewed. CONCLUSIONS:  An item association-based recommender algorithm can predict appropriate specialists' workup orders with high discriminatory accuracy. This could support future clinical decision support tools to increase effectiveness and access to specialty referrals. Our study demonstrates important first steps toward a data-driven paradigm for outpatient specialty consultation with a tier of automated recommendations that proactively enable initial workup that would otherwise be delayed by awaiting an in-person visit.

A new approach of H∞ filtering for combustion systems using optical instrumentation.

This paper proposes a new filtering scheme applied to a linearized model of a nonlinear representation for combustion systems, whose parameters are obtained by means of optical sensors. To ensure a robust representation regarding the chosen operation point and external disturbances variations, a linear parameter-varying (LPV) state-space representation is proposed in terms of noise disturbances and time-varying parameters affecting the plant (like the instrumentation noise and non-laminar air flow). Concerning the proposed filtering scheme, a new observer structure, which includes the incorporation of the control signal as an additional input of the filter, is proposed to assure improved stability margins and performance given in terms of the H∞ norm. The filter design method is based on a convex optimization technique and is capable to deal with unstable dynamics. A numerical experiment, whose data were obtained from an actual combustion plant, illustrates the flexibility and advantages of the method when compared with the maximum correntropy criterion based Kalman filter, the full-order filter and the standard Luenberger observer.

A Novel Theoretical Probabilistic Model for Opportunistic Routing with Applications in Energy Consumption for WSNs.

This paper proposes a new theoretical stochastic model based on an abstraction of the opportunistic model for opportunistic networks. The model is capable of systematically computing the network parameters, such as the number of possible routes, the probability of successful transmission, the expected number of broadcast transmissions, and the expected number of receptions. The usual theoretical stochastic model explored in the methodologies available in the literature is based on Markov chains, and the main novelty of this paper is the employment of a percolation stochastic model, whose main benefit is to obtain the network parameters directly. Additionally, the proposed approach is capable to deal with values of probability specified by bounded intervals or by a density function. The model is validated via Monte Carlo simulations, and a computational toolbox (R-packet) is provided to make the reproduction of the results presented in the paper easier. The technique is illustrated through a numerical example where the proposed model is applied to compute the energy consumption when transmitting a packet via an opportunistic network.

Robust Static Output-Feedback Control for MJLS with Non-Homogeneous Markov Chains: A Comparative Study Considering a Wireless Sensor Network with Time-Varying PER.

This paper deals with the problem of control through a semi-reliable communication channel, such as wireless sensor networks (WSN). Particularly, the case investigated is the one where the packet loss rate of the network is time-varying due to, for instance, variation in the distance between the nodes. Considering this practical motivation, the control system is modeled using a formulation based on discrete-time Markov jump linear systems (MJLS) with non-homogeneous Markov chains (time-varying transition probabilities). New control design conditions based on parameter-dependent linear matrix inequalities are proposed in order to solve this problem. The purpose is to demonstrate that this strategy is suitable to handle the networked control problem by comparing the temporal behavior of the closed-loop system with the Markovian controller and a standard proportional-integral-derivative (PID) controller. The case study presented in the paper considers the problem of the remote control of a Vertical Take-Off and Landing (VTOL) vehicle through a wireless communication channel. The network packet loss model employed in the case study is based on data collected on a wireless network workbench, which was previously developed and validated by the authors.

Development and validation of high definition phenotype-based mortality prediction in critical care units.

OBJECTIVES: The objectives of this study are to construct the high definition phenotype (HDP), a novel time-series data structure composed of both primary and derived parameters, using heterogeneous clinical sources and to determine whether different predictive models can utilize the HDP in the neonatal intensive care unit (NICU) to improve neonatal mortality prediction in clinical settings. MATERIALS AND METHODS: A total of 49 primary data parameters were collected from July 2018 to May 2020 from eight level-III NICUs. From a total of 1546 patients, 757 patients were found to contain sufficient fixed, intermittent, and continuous data to create HDPs. Two different predictive models utilizing the HDP, one a logistic regression model (LRM) and the other a deep learning long-short-term memory (LSTM) model, were constructed to predict neonatal mortality at multiple time points during the patient hospitalization. The results were compared with previous illness severity scores, including SNAPPE, SNAPPE-II, CRIB, and CRIB-II. RESULTS: A HDP matrix, including 12 221 536 minutes of patient stay in NICU, was constructed. The LRM model and the LSTM model performed better than existing neonatal illness severity scores in predicting mortality using the area under the receiver operating characteristic curve (AUC) metric. An ablation study showed that utilizing continuous parameters alone results in an AUC score of >80% for both LRM and LSTM, but combining fixed, intermittent, and continuous parameters in the HDP results in scores >85%. The probability of mortality predictive score has recall and precision of 0.88 and 0.77 for the LRM and 0.97 and 0.85 for the LSTM. CONCLUSIONS AND RELEVANCE: The HDP data structure supports multiple analytic techniques, including the statistical LRM approach and the machine learning LSTM approach used in this study. LRM and LSTM predictive models of neonatal mortality utilizing the HDP performed better than existing neonatal illness severity scores. Further research is necessary to create HDP-based clinical decision tools to detect the early onset of neonatal morbidities.

Designing a bed-side system for predicting length of stay in a neonatal intensive care unit.

Increased length of stay (LOS) in intensive care units is directly associated with the financial burden, anxiety, and increased mortality risks. In the current study, we have incorporated the association of day-to-day nutrition and medication data of the patient during its stay in hospital with its predicted LOS. To demonstrate the same, we developed a model to predict the LOS using risk factors (a) perinatal and antenatal details, (b) deviation of nutrition and medication dosage from guidelines, and (c) clinical diagnoses encountered during NICU stay. Data of 836 patient records (12 months) from two NICU sites were used and validated on 211 patient records (4 months). A bedside user interface integrated with EMR has been designed to display the model performance results on the validation dataset. The study shows that each gestation age group of patients has unique and independent risk factors associated with the LOS. The gestation is a significant risk factor for neonates < 34 weeks, nutrition deviation for < 32 weeks, and clinical diagnosis (sepsis) for ≥ 32 weeks. Patients on medications had considerable extra LOS for ≥ 32 weeks' gestation. The presented LOS model is tailored for each patient, and deviations from the recommended nutrition and medication guidelines were significantly associated with the predicted LOS.

Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study.

Our objective in this study was to determine if machine learning (ML) can automatically recognize neonatal manipulations, along with associated changes in physiological parameters. A retrospective observational study was carried out in two Neonatal Intensive Care Units (NICUs) between December 2019 to April 2020. Both the video and physiological data (heart rate (HR) and oxygen saturation (SpO2)) were captured during NICU hospitalization. The proposed classification of neonatal manipulations was achieved by a deep learning system consisting of an Inception-v3 convolutional neural network (CNN), followed by transfer learning layers of Long Short-Term Memory (LSTM). Physiological signals prior to manipulations (baseline) were compared to during and after manipulations. The validation of the system was done using the leave-one-out strategy with input of 8 s of video exhibiting manipulation activity. Ten neonates were video recorded during an average length of stay of 24.5 days. Each neonate had an average of 528 manipulations during their NICU hospitalization, with the average duration of performing these manipulations varying from 28.9 s for patting, 45.5 s for a diaper change, and 108.9 s for tube feeding. The accuracy of the system was 95% for training and 85% for the validation dataset. In neonates <32 weeks' gestation, diaper changes were associated with significant changes in HR and SpO2, and, for neonates ≥32 weeks' gestation, patting and tube feeding were associated with significant changes in HR. The presented system can classify and document the manipulations with high accuracy. Moreover, the study suggests that manipulations impact physiological parameters.

Axonal Conduction Velocity in CA1 Area of Hippocampus is Reduced in Mouse Models of Alzheimer's Disease.

The timing of action potentials arrival at synaptic terminals partially determines integration of synaptic inputs and is important for information processing in the CNS. Therefore, axonal conduction velocity (VC) is a salient parameter, influencing the timing of synaptic inputs. Even small changes in VC may disrupt information coding in networks requiring accurate timing. We recorded compound action potentials in hippocampal slices to measure VC in three mouse models of Alzheimer's disease. We report an age-dependent reduction in VC in area CA1 in two amyloid-ß precursor protein transgenic mouse models, line 41 and APP/PS1, and in a tauopathy model, rTg4510.

Clinical decision support tool for phototherapy initiation in preterm infants.

OBJECTIVE: Adherence to guidelines for phototherapy initiation in preterm infants was 39% in our academic NICU (61% of phototherapy was initiated at total bilirubin (TB) levels below recommended thresholds). We hypothesized that adoption of an electronic health record integrated clinical decision support (CDS) tool would improve adherence to phototherapy guidelines. STUDY DESIGN: We developed and implemented Premie BiliRecs (PBR), a novel CDS tool for phototherapy initiation in preterm infants from 27 through 34 weeks postmenstrual age. The primary outcome measure was the proportion of phototherapy initiation events consistent with recommended TB thresholds. RESULT: Following the implementation of PBR, adherence to guidelines for phototherapy initiation in preterm infants increased to 69.8% (p < 0.001), an improvement of 77%. There was no increase in the incidence of severe hyperbilirubinemia nor exchange transfusions. CONCLUSION: The adoption of PBR was associated with improved adherence to phototherapy guidelines in preterm infants without increased adverse events.

Development of data dictionary for neonatal intensive care unit: advancement towards a better critical care unit.

BACKGROUND: Critical care units (CCUs) with extensive use of various monitoring devices generate massive data. To utilize the valuable information of these devices; data are collected and stored using systems like clinical information system and laboratory information management system. These systems are proprietary, allow limited access to their database and, have the vendor-specific clinical implementation. In this study, we focus on developing an open-source web-based meta-data repository for CCU representing stay of the patient with relevant details. METHODS: After developing the web-based open-source repository named data dictionary (DD), we analyzed prospective data from 2 sites for 4 months for data quality dimensions (completeness, timeliness, validity, accuracy, and consistency), morbidity, and clinical outcomes. We used a regression model to highlight the significance of practice variations linked with various quality indicators. RESULTS: DD with 1555 fields (89.6% categorical and 11.4% text fields) is presented to cover the clinical workflow of a CCU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 88% completeness, 97% accuracy, 91% timeliness, and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicators and practice variations are strongly correlated (P < 0.05). CONCLUSION: This study documents DD for standardized data collection in CCU. DD provides robust data and insights for audit purposes and pathways for CCU to target practice improvements leading to specific quality improvements.

Early experiences with combined fellowship training in clinical informatics.

Given the ubiquitous nature of information systems in modern health care, interest in the pursuit of formal training in clinical informatics is increasing. This interest is not restricted to generalists-informatics training is increasingly being sought by future subspecialists. The traditional structure of Accreditation Council on Graduate Medical Education subspecialty training requires completion of both clinical and clinical informatics fellowship programs, and understandably lacks appeal due to the time commitment required. One approach to encourage clinical informatics training is to integrate it with clinical fellowships in order to confer dual-board eligibility. In this perspective, we describe 3 successful petitions for combined training in clinical informatics in order to support other programs and the American Board of Preventive Medicine in establishing pathways for training subspecialists in clinical informatics.

Your Patient Has a New Health App? Start With Its Data Source.

Recent regulatory and technological advances have enabled a new era of health apps that are controlled by patients and contain valuable health information. These health apps will be numerous and use novel interfaces that appeal to patients but will likely be unfamiliar to practitioners. We posit that understanding the origin of the health data is the most meaningful and versatile way for physicians to understand and effectively use these apps in patient care. This will allow providers to better support patients and encourage patient engagement in their own care.

Protocol for Energy-Efficiency in Networked Control Systems Based on WSN.

This paper proposes a new communication protocol for output-feedback control through multi-hop Wireless Sensor Network (WSN). The protocol is based on a Hop-by-Hop transport scheme and is especially devised to simultaneously fulfill two conflicting criteria: the network energy consumption and the stability/performance (in terms of H∞ norm) of the closed-loop system. The proposed protocol can be implemented by means of three heuristics, basically using distinct rules to control the maximum number of retransmissions allowed in terms of the voltage level of the batteries of the network nodes. As another contribution, a Markov jump based representation is proposed to model the packet loss in the communication channel, giving rise to a systematic procedure to determine the transition probability matrix and the Markov chain operation modes of a network with multiple information sources. The synthesis of the output-feedback controller is made in two steps (observer filter plus a state-feedback controller) for the Markov model assuming partial availability of the operation modes. The efficiency and applicability of the communication protocol is illustrated by means of a numerical experiment, based on a physical model of a coupled tanks plant. The features of each heuristic of implementation of the proposed protocol are presented in the numerical comparisons.

Supraspinal interaction between HIV-1-gp120 and cannabinoid analgesic effectiveness.

The growing therapeutic use (self-medication) of cannabinoids by HIV-1 infected people and the recent interest in the possible medicinal use of cannabinoids, particularly in pain management, create an urgent need to identify their potential interactions with HIV-1. The goal here is to determine any interaction between proteins of HIV-1 and the analgesic effectiveness of cannabinoid at supraspinal level. Young adult male rats (Sprague-Dawley) were stereotaxically pretreated with HIV-1 envelope glycoprotein 120 (gp120) into the periaqueductal gray (PAG) area, the primary control center of pain modulation. Then, we examined its effect on cannabinoid receptor agonist WIN55,212-2-induced analgesia. Our results demonstrated that gp120 in PAG diminished the analgesic effectiveness of this cannabinoid agonist. These results suggest that gp120 may interact with the cannabinoid system through the descending modulatory pain pathways centered in the PAG to impair the analgesic effectiveness of cannabinoids.

Prenatal treatment of ornithine transcarbamylase deficiency.

PURPOSE OF STUDY: Patients with neonatal urea cycle defects (UCDs) typically experience severe hyperammonemia during the first days of life, which results in serious neurological injury or death. Long-term prognosis despite optimal pharmacological and dietary therapy is still poor. The combination of intravenous sodium phenylacetate and sodium benzoate (Ammonul®) can eliminate nitrogen waste independent of the urea cycle. We report attempts to improve outcomes for males with severe ornithine transcarbamylase deficiency (OTCD), a severe X-linked condition, via prenatal intravenous administration of Ammonul and arginine to heterozygous carrier females of OTCD during labor. METHODS USED: Two heterozygote OTCD mothers carrying male fetuses with a prenatal diagnosis of OTCD received intravenous Ammonul, arginine and dextrose-containing fluids shortly before birth. Maintenance Ammonul and arginine infusions and high-caloric enteral nutrition were started immediately after birth. Ammonul metabolites were measured in umbilical cord blood and the blood of the newborn immediately after delivery. Serial ammonia and biochemical analyses were performed following delivery. SUMMARY OF RESULTS: Therapeutic concentrations of Ammonul metabolites were detected in umbilical cord and neonatal blood samples. Plasma ammonia and glutamine levels in the postnatal period were within the normal range. Peak ammonia levels in the first 24-48h were 53mcmol/l and 62mcmol/l respectively. The boys did not experience neurological sequelae secondary to hyperammonemia and received liver transplantation at ages 3months and 5months. The patients show normal development at ages 7 and 3years. CONCLUSION: Prenatal treatment of mothers who harbor severe OTCD mutations and carry affected male fetuses with intravenous Ammonul and arginine, followed by immediate institution of maintenance infusions after delivery, results in therapeutic levels of benzoate and phenylacetate in the newborn at delivery and, in conjunction with high-caloric enteral nutrition, prevents acute hyperammonemia and neurological decompensation. Following initial medical management, early liver transplantation may improve developmental outcome.

Differences in Synaptic Dysfunction Between rTg4510 and APP/PS1 Mouse Models of Alzheimer's Disease.

Genetically modified mice have provided insights into the progression and pathology of Alzheimer's disease (AD). Here, we have examined two mouse models of AD: the rTg4510 mouse, which overexpresses mutant human Tau gene, and the APP/PS1 mouse, which overexpresses mutant human genes for amyloid precursor protein and presenilin 1. Both models exhibit deficits in hippocampal function, but comparative analyses of these deficits are sparse. We used extracellular field potential recordings in hippocampal slices to study basal synaptic transmission (BST), paired-pulse facilitation (PPF), and long-term potentiation (LTP) at the Schaffer collateral-CA1 pyramidal cell synapses in both models. We found that 6-7, but not 2-3-month-old rTg4510 mice exhibited reduced pre-synaptic activation (fiber volley (FV) amplitude, ∼50%) and field excitatory post-synaptic potential (fEPSP) slope (∼40%) compared to wild-type controls. In contrast to previous reports, BST, when controlled for FV amplitude, was not altered in rTg4510. APP/PS1 mice (2-3 mo and 8-10 mo) had unchanged FV amplitude compared to wild-type controls, while fEPSP slope was reduced by ∼34% in older mice, indicating a deficit in BST. PPF was unchanged in 8-10-month-old APP/PS1 mice, but was reduced in 6-7-month-old rTg4510 mice. LTP was reduced only in older rTg4510 and APP/PS1 mice. Our data suggest that BST deficits appear earlier in APP/PS1 than in rTg4510, which exhibited no BST deficits at the ages tested. However, FV and synaptic plasticity deficits developed earlier in rTg4510. These findings highlight fundamental differences in the progression of synaptic pathology in two genetically distinct models of AD.