The Power and Promise of Angiotensin Receptor Neprilysin Inhibitor (ARNI) in Heart Failure Management: National Consensus Statement.

;Heart failure (HF) is a huge global public health task due to morbidity, mortality, disturbed quality of life, and major economic burden. It is an area of active research and newer treatment strategies are evolving. Recently angiotensin receptor-neprilysin inhibitor (ARNI), a class of drugs (the first agent in this class, Sacubitril-Valsartan), reduces cardiovascular mortality and morbidity in chronic HF patients with reduced left ventricular ejection fraction (LVEF). Positive therapeutic effects have led to a decrease in cardiovascular mortality and HF hospitalizations (HFH), with a favorable safety profile, and have been documented in several clinical studies with an unquestionable survival benefit with ARNI, Sacubitril-Valsartan. This consensus statement of the Indian group of experts in cardiology, nephrology, and diabetes provides a comprehensive review of the power and promise of ARNI in HF management and an evidence-based appraisal of the use of ARNI as an essential treatment strategy for HF patients in clinical practice. Consensus in this review favors an early utility of Sacubitril-Valsartan in patients with HF with reduced EF (HFrEF), regardless of the previous therapy being given. A lower rate of hospitalizations for HF with Sacubitril-Valsartan in HF patients with preserved EF who are phenotypically heterogeneous suggests possible benefits of ARNI in patients having 40-50% of LVEF, frequent subtle systolic dysfunction, and higher hospitalization risk.

Bacteriophages: A possible solution to combat enteropathogenic Escherichia coli infections in neonatal goats.

Due to awareness and benefits of goat rearing in developing economies, goats' significance is increasing. Unfortunately, these ruminants are threatened via multiple bacterial pathogens such as enteropathogenic Escherichia coli (EPEC). In goat kids and lambs, EPEC causes gastrointestinal disease leading to substantial economic losses for farmers and may also pose a threat to public health via the spread of zoonotic diseases. Management of infection is primarily based on antibiotics, but the need for new therapeutic measures as an alternative to antibiotics is becoming vital because of the advent of antimicrobial resistance (AMR). The prevalence of EPEC was established using bfpA gene, uspA gene and Stx1 gene, followed by phylogenetic analysis using Stx1 gene. The lytic activity of the isolated putative coliphages was tested on multi-drug resistant strains of EPEC. It was observed that a PCR based approach is more effective and rapid as compared to phenotypic tests of Escherichia coli virulence. It was also established that the isolated bacteriophages exhibited potent antibacterial efficacy in vitro, with some of the isolates (16%) detected as T4 and T4-like phages based on gp23 gene. Hence, bacteriophages as therapeutic agents may be explored as an alternative to antibiotics in managing public, livestock and environmental health in this era of AMR.

Arterial and venous thrombosis in a patient with Covid-19: A unique presentation.

Severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) infection not only affects the respiratory system but also induces coagulation abnormalities and thrombosis. We report a middle-aged woman who presented during the Covid-19 pandemic with sudden-onset acute left upper limb ischaemia of short duration, with no history of dry cough, breathlessness or fever, and tested positive on TrueNAT for SARS-CoV-2. Later, she developed deep venous thrombosis of the right lower limb during isolation in the hospital.

Frequency Domain Analysis of Multiwavelength Photoacoustic Signals for Differentiating Among Malignant, Benign, and Normal Thyroids in an Ex Vivo Study With Human Thyroids.

OBJECTIVES: This study investigated the capability of spectral parameters, extracted by frequency domain analysis of photoacoustic signals, to differentiate among malignant, benign, and normal thyroid tissue. METHODS: We acquired multiwavelength photoacoustic images of freshly excised thyroid specimens collected from 50 patients who underwent thyroidectomy after having a diagnosis of suspected thyroid lesions. A thyroid cytopathologist marked histologic slides of each tissue specimen. These marked slides were used as ground truth to identify the regions of interest (ROIs) corresponding to malignant, benign, and normal thyroid tissue. Three spectral parameters: namely, slope, midband fit, and intercept, were extracted from photoacoustic signals corresponding to different ROIs. RESULTS: Spectral parameters were extracted from a total of total of 65 ROIs. According to the ground truth, 12 of 65 ROIs belonged to malignant thyroids; 28 of 65 ROIs belonged to benign thyroids; and 25 of 65 ROIs belonged to normal thyroids. Besides slope, the other 2 spectral parameters and grayscale photoacoustic image pixel values were found to be significantly different (P < .05) between malignant and normal thyroids. Between benign and normal thyroids, all 3 spectral parameters and photoacoustic pixel values were significantly different (P < .05). CONCLUSIONS: Preliminary results of our ex vivo human thyroid study show that the spectral parameters extracted from radiofrequency photoacoustic signals as well as the pixel values of 2-dimensional photoacoustic images can be used for differentiating among malignant, benign, and normal thyroid tissue.

Temporal Trends of Cardiac Chambers Function with Trastuzumab in Human Epidermal Growth Factor Receptor II-Positive Breast Cancer Patients.

BACKGROUND: Trastuzumab has substantially improved overall survival and reduced the risk of disease recurrence in patients with human epidermal growth factor receptor type II (HER-II)-positive breast cancer. However, this benefit may be at the increased risk of cardiotoxicity. We aimed to explore the early subclinical left and right ventricular as well as atrial dysfunction, in trastuzumab-treated patients with HER-II-positive breast cancer, using velocity vector imaging. METHODS: Echocardiography images were acquired in 50 patients with HER-II-positive breast cancer undergoing trastuzumab therapy. All patients had baseline and 3-6 months and 12-15 months of follow-up echocardiograms after initiation of trastuzumab therapy. Subendocardial borders of all the cardiac chambers were traced from the apical views to obtain volumetric and deformation indices. RESULTS: Mean age was 60 ± 13 years. Left ventricular (LV) ejection fraction as well as conventional indices of right ventricular (RV) function did not change with trastuzumab. The RV peak systolic global longitudinal strain (GLε) significantly decreased (-24.53 ± 6.03 vs. -21.28 ± 5.11 vs. -21.84 ± 5.15, baseline vs. first and second follow-ups, P = 0.01). LV peak systolic GLε was reduced by 1.19 at early follow-up (P < 0.05). Left atrial reservoir and booster pump functions as well as right atrial reservoir function were reduced through follow-up as well. CONCLUSIONS: The RV exhibited greater change in strain after trastuzumab treatment when compared to the LV. Atria function was reduced by trastuzumab as well. The repercussion of these findings and their potential implication will warrant further study.

Evaluation of Frequency Domain Analysis of a Multiwavelength Photoacoustic Signal for Differentiating Malignant From Benign and Normal Prostates: Ex Vivo Study With Human Prostates.

OBJECTIVES: The purpose of this study was to investigate the feasibility of differentiating malignant prostate from benign prostatic hyperplasia (BPH) and normal prostate tissue by performing frequency domain analysis of photoacoustic images acquired at 2 different wavelengths. METHODS: We performed multiwavelength photoacoustic imaging on freshly excised human prostate specimens taken from a total of 30 patients undergoing prostatectomy for biopsy-confirmed prostate cancer. Histologic slides marked by a genitourinary pathologist were used as ground truth to define regions of interest (ROIs) in the photoacoustic images. Primarily, 3 different prostate tissue categories, namely malignant, BPH, and normal, were considered, while a fourth category named nonmalignant was formed by combining the ROIs corresponding to BPH and normal tissue together. We extracted 3 spectral parameters, namely slope, midband fit, and intercept, from power spectra of the radiofrequency photoacoustic signals corresponding to the 3 primary tissue categories. RESULTS: We analyzed data from 53 ROIs selected from the photoacoustic images of 30 patients. According to the histopathologic analysis, 19 ROIs were malignant, 8 were BPH, and 26 were normal. All the 3 spectral parameters and C-scan grayscale photoacoustic image pixel values were found to be significantly different (P < .01) between malignant and nonmalignant prostate as well as malignant and normal prostate. CONCLUSIONS: Preliminary results of our ex vivo human prostate study suggest that spectral parameters obtained by performing frequency domain analysis of photoacoustic signals can be used to differentiate between malignant and nonmalignant prostate.

Predictors of incipient dysfunction of all cardiac chambers after treatment of metastatic renal cell carcinoma by tyrosine kinase inhibitors.

PURPOSE: We aimed to explore the hypothesis that early subclinical cardiac chamber dysfunction secondary to tyrosine kinase inhibitors (TKIs) in patients with metastatic renal cell carcinoma could be signaled by abnormal cardiac mechanics demonstrated by velocity vector imaging. METHODS: Echocardiographic images were acquired from the apical views in 23 metastatic renal cell carcinoma patients. All patients had baseline and at least a 3-month follow-up echocardiogram after receiving TKI therapy. Subendocardial borders of all the cardiac chambers were traced to obtain volumetric and deformation indices. RESULTS: Mean age was 67 ± 9 years with 92% men. The right ventricle peak systolic global longitudinal strain (GLɛ) and strain rate were significantly lower after TKIs (-23.49 ± 5.1 versus -19.81 ± 5.5, p = 0.002 and -1.52 ± 0.52 versus -1.24 ± 0.35 p = 0.02, respectively). LV GLɛ was not statistically different. Volumetric and deformation indices showed a minimal decrease of the right atrium reservoir and conduit functions, and no significant changes of left atrial function. CONCLUSIONS: The right heart exhibited greater strain changes than the left heart after TKI treatment. The implications of these findings and their potential significance warrant further work.

Multiple cutaneous, oropharyngeal and pulmonary nodules.

Erdheim-Chester disease (ECD) is a rare non-Langerhans cell histiocytosis defined by heterogeneous multiorgan involvement. Due to the rarity of this disease strong evidence-based therapies have yet to be established and prognosis has previously been considered to be poor, with more than half of patients dying within 3 years of initial presentation. We describe an 86-year-old woman with a 34-year history of extensive cutaneous and internal nodules with typical pathological and immunophenotypical (CD68(+) /CD1a(-) ) features of ECD without evidence of the BRAF V600E mutation. The cosmetic appearance of cutaneous nodules and hoarse voice caused by vocal cord nodules has been managed surgically. More aggressive therapies reported for ECD were trialled for this patient, such as radiotherapy and interferon-α, with no response. This case demonstrates a relatively good prognosis in ECD that has been managed conservatively.

Preliminary results of ex vivo multispectral photoacoustic imaging in the management of thyroid cancer.

OBJECTIVE: The purpose of this study was to validate whether ex vivo multispectral photoacoustic imaging can be used to differentiate malignant tissue, benign nodules, and normal human thyroid tissue. SUBJECTS AND METHODS: Fifty patients undergoing thyroidectomy because of thyroid lesions participated in this study. Multispectral photoacoustic imaging was performed on surgically excised thyroid tissue, and chromophore images that represented optical absorption of deoxyhemoglobin, oxyhemoglobin, lipid, and water were reconstructed. After the imaging procedure, the pathologist marked malignant tissue, benign nodules, and normal regions on histopathologic slides, and digital images of the marked histopathologic slides were obtained. The histopathologic images were coregistered with chromophore images. Areas corresponding to malignant tissue, benign nodules, and normal tissue were defined on the chromophore images. Pixel values within each area were averaged to determine the mean intensities of deoxyhemoglobin, oxyhemoglobin, lipid, and water. RESULTS: There was a statistically significant difference between malignant and benign nodules with respect to mean intensity of deoxyhemoglobin (p = 0.014). There was a difference between malignant and normal tissue in mean intensity of deoxyhemoglobin (p = 0.003), lipid (p = 0.001), and water (p < 0.0001). A difference between benign nodules and normal tissue was found in mean intensity of oxyhemoglobin (p < 0.0001), lipid (p < 0.0001), and water (p < 0.0001). The sensitivity, specificity, and positive and negative predictive values of the system tested in differentiating malignant from nonmalignant thyroid tissue were 69.2%, 96.9%, 81.8%, and 93.9%. CONCLUSION: The preliminary results of this ex vivo human thyroid study suggest that multispectral photoacoustic imaging can be used to differentiate malignant and benign nodules and normal human thyroid tissue.

Emerging analytical approaches for personalized medicine using machine learning in pediatric and congenital heart disease.

Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists despite still being an emerging field. While precision medicine relies most often on the underlying biology/pathophysiology of a patient's condition, personalized medicine rely on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often general through machine learning algorithms. There are a number of analytical considerations regarding the creation of digital biomarkers that are discussed in this review, including: data pre-processing, time dependency and gating, dimensionality reduction and novel methods both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance are particularly challenging in small and heterogenous populations with rare outcomes like children with congenital heart disease. Finally, we review analytical considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical AI operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that despite these analytical challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.

Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach.

IMPORTANCE AND OBJECTIVES: The current medical paradigm of evidence-based medicine relies on clinical guidelines derived from randomized clinical trials (RCTs), but these guidelines often overlook individual variations in treatment effects. Approaches have been proposed to develop models predicting the effects of individualized management, such as predictive allocation, individualizing treatment allocation. It is currently unknown whether widespread implementation of predictive allocation could result in better population-level outcomes over guideline-based therapy. We sought to simulate the potential effect of predictive allocation using data from previously conducted RCTs. METHODS AND RESULTS: Data from 3 RCTs (positive trial, negative trial, trial stopped for futility) in pediatric cardiology were used in a computational simulation study to quantify the potential benefits of a personalized approach based on predictive allocation. Outcomes were compared when using a universal approach vs predictive allocation where each patient was allocated to the treatment associated with the lowest predicted probability of negative outcome. Compared to results from RCTs, predictive allocation yielded absolute risk reductions of 13.8% (95% confidence interval [CI] -1.9 to 29.5), 13.9% (95% CI 4.5-23.2), and 15.6% (95% CI 1.5-29.6), respectively, corresponding to a number needed to treat of 7.3, 7.2, and 6.4. The net benefit of predictive allocation was directly proportional to the performance of the prediction models and disappeared as model performance degraded below an area under the curve of 0.55. DISCUSSION: These findings highlight that predictive allocation could result in improved group-level outcomes, particularly when highly predictive models are available. These findings will need to be confirmed in simulations of other trials with varying conditions and eventually in RCTs of predictive vs guideline-based treatment allocation.

Multidrug-resistant coagulase-negative Staphylococci in food animals.

AIMS: To study the antimicrobial resistance of coagulase-negative staphylococci (CoNS) in animals. METHODS AND RESULTS: In the present study, a total of 87 CoNS recovered from food animals were characterized by antimicrobial susceptibility testing, resistance gene identification and conjugation. Of the seven species studied, Staphylococcus lentus, Staphylococcus sciuri, Staphylococcus xylosus and Staphylococcus haemolyticus accounted for over 96% of the isolates. In addition to ß-lactam resistance (100%), high percentages of CoNS were resistant to tetracycline (67·8%), erythromycin (36·7%), clindamycin (27·5%) and quinopristin/dalfopristin (14·9%). Importantly, 47 (54%) isolates were resistant to at least three antimicrobial classes, including six CoNS resistant to six antimicrobial classes. The common genes for the above-mentioned resistance phenotypes were mec(A), tet(M), erm(A) and vga(A)(LC) , which were identified from 68·7%, 61%, 56·2% and 69·2% of the isolates, respectively. tet(M) was conjugatively transferable from 10 tetracycline-resistant CoNS to a Enterococcus strain, underlining the potential of antimicrobial resistance transfer from Staphylococcus to the commensal bacteria in human. CONCLUSIONS: Multidrug resistance and resistance to non-ß-lactam antimicrobials are common in CoNS in animals. SIGNIFICANCE AND IMPACT OF THE STUDY: The data improve our understanding on the extent to which CoNS contribute to the dissemination of antimicrobial resistance in the food production environment.

3-Methoxy-4-hydroxyphenylglycol in cerebrospinal fluid and vanillylmandelic acid in urine of humans with hypertension.

3-Methoxy-4-hydroxyphenylglycol (MHPG) was measured in lumbar spinal fluid of 20 subjects with hypertension of varied etiology and severity. There was a significant correlation between the concentration of MHPG and the severity of hypertension. However, changes in the concentration of vanillylmandelic acid in the urine of these subjects were insignificant. In six subjects, administration of clonidine or alpha-methyldopa, two centrally acting antihypertensive drugs, was associated with a significant lowering of MHPG concentrations. These data support the hypothesis that central catecholamines are involved in clinical hypertension.

Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients are readmitted to hospitals if they are diagnosed with one of the six conditions mentioned in the Act. However, patients diagnosed with lupus are the sixth highest in terms of rehospitalization. The heterogeneity in the disease and patient characteristics makes it very hard to predict rehospitalization. This research utilizes deep learning methods to predict rehospitalization within 30 days by extracting the temporal relationships in the longitudinal EHR clinical data. Prediction results from deep learning methods such as LSTM are evaluated and compared with traditional classification methods such as penalized logistic regression and artificial neural networks. The simple recurrent neural network method and its variant, gated recurrent unit network, are also developed and validated to compare their performance against the proposed LSTM model. The results indicated that the deep learning method RNN-LSTM has a significantly better performance (with an AUC of .70) compared to traditional classification methods such as ANN (with an AUC of 0.66) and penalized logistic regression (with an AUC of 0.63). The rationale for the better performance of the deep learning method may be due to its ability to leverage the temporal relationships of the disease state in patients over time and to capture the progression of the disease-relevant clinical information from patients' prior visits is carried forward in the memory, which may have enabled the higher predictability for the deep learning methods.