Therapeutic Decisions
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2022 ◽  
Vol 13 (1) ◽  
pp. 172-179
Laté Mawuli Lawson-Ananissoh ◽  
Aklesso Bagny ◽  
Oumboma Bouglouga ◽  
Laconi Kaaga ◽  
Gad Namdiro ◽  

Background: The process of hepatic fibrosis is common to the various etiologies of chronic liver disease such as viral hepatitis B. Objective: To evaluate hepatic fibrosis by non-invasive markers such as Aspartate-to-Platelet Ratio Index (APRI), fibrosis-4 (FIB-4), fibrotest and fibroscan. Patients and Method: This was a descriptive study during a period of 32 months. Included in our study were the records of outpatients, chronic carriers of hepatitis B virus without viral co-infection C, D or HIV, followed in the Gastroenterology unit of the Campus Teaching Hospital of Lome-Togo. Results: We retained 222 patients. Among the patients, 148 patients (66.67%) were classified in Phase 3 (inactive carrying). Only 10 patients (4.50%) had a APRI score indicating a fibrosis stage ≥ F4 (presence of cirrhosis). A FIB-4 score indicating the presence of cirrhosis was found in 12 patients (5.40%). The most represented stage at fibrotest was the F0 stage (45.45%). Cirrhosis was noted in 6.06% of cases at fibroscan. Patients with APRI score ≤ 2 (96.23%) had a FIB-4 score ≤ 3.25, (p = 0.0088). Conclusion: The evaluation of hepatic fibrosis during chronic hepatopathies is essential for patients care because it influences therapeutic decisions.

2022 ◽  
pp. 107815522110736
Ioannis A. Voutsadakis

Objective Everolimus is an inhibitor of serine/ threonine kinase mTOR. The drug is approved for the treatment of metastatic ER positive, HER2 negative breast cancers and benefits a subset of patients with these breast cancers in combination with hormonal therapies. Despite extensive efforts, no additional predictive biomarkers to guide therapeutic decisions for everolimus have been introduced in clinical practice. Data sources This paper discusses predictive biomarkers for everolimus efficacy in breast cancer. A search of the medline and web of science databases was performed using the words “everolimus” and “biomarkers”. References of retrieved articles were manually scanned for additional relevant articles. Data Summary Everolimus benefits a subset of patients with metastatic ER positive, HER2 negative breast cancers in combination with hormonal therapies. Despite extensive efforts no additional predictive biomarkers to guide therapeutic decisions for everolimus therapy have been confirmed for use in clinical practice. However, promising biomarker leads for everolimus efficacy in breast cancer have been suggested and include expression of proteins in the mTOR pathway in ER positive, HER2 negative breast cancers. In HER2 positive cancers PIK3CA mutations, and PTEN expression loss are prognostic. Other clinical predictive biomarkers with more limited data include characteristics derived from whole genome sequencing, subsets of circulating leukocytes and changes in Standardized Uptake Values (SUV) of Positron Emission Tomography (PET) scans. Conclusions Putative predictive biomarkers for everolimus efficacy in breast cancer patients, both genomic and clinical, deserve further study and could lead to a better selection of responsive patients.

2022 ◽  
Vol 16 (1) ◽  
pp. e0010070
Izabela Jardim Rodrigues Pitta ◽  
Mariana de Andrea Vilas-Boas Hacker ◽  
Ligia Rocha Andrade ◽  
Clarissa Neves Spitz ◽  
Robson Teixeira Vital ◽  

Introduction Pure Neural Leprosy (PNL) is a rare clinical form of leprosy in which patients do not present with the classical skin lesions but have a high burden of the disability associated with the disease. Clinical characteristics and follow up of patients in PNL are still poorly described in the literature. Objective This paper aims to describe the clinical, electrophysiological and histopathological characteristics of PNL patients, as well as their evolution after multidrug therapy (MDT). Methods Fifty-two PNL patients were selected. Clinical, nerve conduction studies (NCS), histopathological and anti-PGL-1serology were evaluated. Patients were also assessed monthly during the MDT. At the end of the MDT, all of the patients had a new neurological examination and 44 were submitted to another NCS. Results Paresthesia was the complaint most frequently reported by patients, and in the neurological examination the most common pattern observed was impairment in sensory and motor examination and a mononeuropathy multiplex. Painful nerve enlargement, a classical symptom of leprosy neuropathy, was observed in a minority of patients and in the motor NCS axonal injuries, alone or in combination with demyelinating features, were the most commonly observed. 88% of the patients did not present any leprosy reaction during MDT. There was no statistically significant difference between the neurological examinations, nor the NCS pattern, performed before and after the MDT. Discussion The classical hallmarks of leprosy neuropathy are not always present in PNL making the diagnosis even more challenging. Nerve biopsy is an important tool for PNL diagnosis as it may guide therapeutic decisions. This paper highlights unique characteristics of PNL in the spectrum of leprosy in an attempt to facilitate the diagnosis and management of these patients.

2022 ◽  
Leah Stevens ◽  
Elizabeth Colglazier ◽  
Claire Parker ◽  
Elena K. Amin ◽  
Hythem Nawaytou ◽  

2022 ◽  
pp. 78-98
Sowmya B. J. ◽  
Pradeep Kumar D. ◽  
Hanumantharaju R. ◽  
Gautam Mundada ◽  
Anita Kanavalli ◽  

Disruptive innovations in data management and analytics have led to the development of patient-centric Healthcare 4.0 from the hospital-centric Healthcare 3.0. This work presents an IoT-based monitoring systems for patients with cardiovascular abnormalities. IoT-enabled wearable ECG sensor module transmits the readings in real-time to the fog nodes/mobile app for continuous analysis. Deep learning/machine learning model automatically detect and makes prediction on the rhythmic anomalies in the data. The application alerts and notifies the physician and the patient of the rhythmic variations. Real-time detection aids in the early diagnosis of the impending heart condition in the patient and helps physicians clinically to make quick therapeutic decisions. The system is evaluated on the MIT-BIH arrhythmia dataset of ECG data and achieves an overall accuracy of 95.12% in classifying cardiac arrhythmia.

2021 ◽  
pp. 563-572
Ingrid Faustine ◽  
Amarila Malik ◽  
Retnosari Andrajati ◽  
Septelia Inawati Wanandi

Corona virus infection (COVID-19) is still an unsolved problem in Indonesia until this year. Apart from Java, other islands, including Sulawesi, were also badly affected. The very high mortality rate in Central Sulawesi (3.36%) poses a challenge for health workers; therefore, they should be well informed and with up-to-date information about correct therapeutic decisions. One of the most common comorbidities that often occurs with the severity and mortality of COVID-19 is hypertension. This study aims to determine the clinical characteristics and severity profile and their relationship with the mortality rate of COVID-19 patients with hypertension in Palu, Central Sulawesi. A total of 185 data on COVID-19 patients undergoing treatment at the Palu City Hospital during 2021 and meeting the criteria were recruited as research samples. Patients were divided into two categories, hypertensive (43%) and non-hypertensive (57%). The results showed that the age group, comorbid diabetes mellitus, cardiovascular, blood pressure, and blood glucose levels showed a significant relationship between the two groups (p < 0.05). The median length of stay was 12 days, with conditions leading to discharge (83%) and death (17%); patients who died were hospitalized in moderate and severe clinical conditions. Age group, liver function, and kidney function were positively correlated with severity and mortality. However, hypertension did not show a significant relationship with the severity and mortality of COVID-19 patients.

2021 ◽  
Vol 2 (1) ◽  
pp. 1-17
Jörn Lötsch ◽  
Dario Kringel ◽  
Alfred Ultsch

The use of artificial intelligence (AI) systems in biomedical and clinical settings can disrupt the traditional doctor–patient relationship, which is based on trust and transparency in medical advice and therapeutic decisions. When the diagnosis or selection of a therapy is no longer made solely by the physician, but to a significant extent by a machine using algorithms, decisions become nontransparent. Skill learning is the most common application of machine learning algorithms in clinical decision making. These are a class of very general algorithms (artificial neural networks, classifiers, etc.), which are tuned based on examples to optimize the classification of new, unseen cases. It is pointless to ask for an explanation for a decision. A detailed understanding of the mathematical details of an AI algorithm may be possible for experts in statistics or computer science. However, when it comes to the fate of human beings, this “developer’s explanation” is not sufficient. The concept of explainable AI (XAI) as a solution to this problem is attracting increasing scientific and regulatory interest. This review focuses on the requirement that XAIs must be able to explain in detail the decisions made by the AI to the experts in the field.

Dariusz Chojeta ◽  
Iwona Smarz-Widelska ◽  
Malgorzata M. Koziol

Abstract Introduction. Urinary tract infection (UTI) is one of the most common types of infection in both hospitalized and outpatient settings. The etiology is mostly bacterial, and the typical causative agent is uropathogenic Escherichia coli. There is a noticeable increase in drug resistance of pathogenic microorganisms. The aim of the study was retrospective analyses of etiological agents of UTI and their antibiotic resistance patterns in Nephrology Unit patients. Material and methods. An infection was diagnosed based on the patient’s symptoms and positive results of urine culture, carried out over 26 months. The clinical material was tested by using the VITEK system, the drug susceptibility of the emerged pathogens was identified. Results. The most common etiological agents of UTI were Gram-negative rods: Escherichia coli (51.23%), Klebsiella spp. (19.3%) and Proteus spp. (13.68%). The analysis of drug resistance profiles of these pathogens showed a high percentage of strains resistant to broad-spectrum penicillins and fluoroquinolones. At the same time, it seems that E. coli isolates presented the most favorable pattern of drug susceptibility in this comparison. Conclusions. The alarming tendency of increasing drug resistance among pathogens causing UTIs to antibiotics such as penicillins or fluoroquinolones prompts a careful choice of drugs in empirical therapies. The most appropriate practice in this regard seems to be meticulous control of nosocomial infections and making therapeutic decisions based on the knowledge of local microbiological data.

Proteomes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 49
Ralph Wendt ◽  
Justyna Siwy ◽  
Tianlin He ◽  
Agnieszka Latosinska ◽  
Thorsten Wiech ◽  

Defective complement activation has been associated with various types of kidney disease. This led to the hypothesis that specific urine complement fragments may be associated with kidney disease etiologies, and disease progression may be reflected by changes in these complement fragments. We investigated the occurrence of complement fragments in urine, their association with kidney function and disease etiology in 16,027 subjects, using mass spectrometry based peptidomics data from the Human Urinary Proteome/Peptidome Database. Twenty-three different urinary peptides originating from complement proteins C3, C4 and factor B (CFB) could be identified. Most C3-derived peptides showed inverse association with estimated glomerular filtration rate (eGFR), while the majority of peptides derived from CFB demonstrated positive association with eGFR. Several peptides derived from the complement proteins C3, C4 and CFB were found significantly associated with specific kidney disease etiologies. These peptides may depict disease-specific complement activation and could serve as non-invasive biomarkers to support development of complement interventions through assessing complement activity for patients’ stratification and monitoring of drug impact. Further investigation of these complement peptides may provide additional insight into disease pathophysiology and could possibly guide therapeutic decisions, especially when targeting complement factors.

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