Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning.

Bacteremia refers to the presence of bacteria in the bloodstream, which can lead to a serious and potentially life-threatening condition. In oncology patients, individuals undergoing cancer treatment have a higher risk of developing bacteremia due to a weakened immune system resulting from the disease itself or the treatments they receive. Prompt and accurate detection of bacterial infections and monitoring the effectiveness of antibiotic therapy are essential for enhancing patient outcomes and preventing the development and dissemination of multidrug-resistant bacteria. Traditional methods of infection monitoring, such as blood cultures and clinical observations, are time-consuming, labor-intensive, and often subject to limitations. This manuscript presents an innovative application of infrared spectroscopy of leucocytes of pediatric oncology patients with bacteremia combined with machine learning to diagnose the etiology of infection as bacterial and simultaneously monitor the efficacy of the antibiotic therapy in febrile pediatric oncology patients with bacteremia infections. Through the implementation of effective monitoring, it becomes possible to promptly identify any indications of treatment failure. This, in turn, indirectly serves to limit the progression of antibiotic resistance. The logistic regression (LR) classifier was able to differentiate the samples as bacterial or control within an hour, after receiving the blood samples with a success rate of over 95 %. Additionally, initial findings indicate that employing infrared spectroscopy of white blood cells (WBCs) along with machine learning is viable for monitoring the success of antibiotic therapy. Our follow up results demonstrate an accuracy of 87.5 % in assessing the effectiveness of the antibiotic treatment.

Factor VII Deficiency in Patients Receiving Chronic Packed Cell Transfusions.

Acquired factor VII deficiency is a rare coagulopathy that has not been reported in transfusion-dependent patients so far. In this study, we reviewed files of 26 transfusion-dependent patients for coagulation profiles, factor V levels, factor VII levels, possible environmental factors influencing factor VII levels, and bleeding history. In 26 of 29 patients (89.6%), we found mild factor VII deficiency (<60%) with levels ranging between 35% and 56%. Bleeding history was unremarkable. We concluded that transfusion-dependent patients may have mild factor VII deficiency with no bleeding tendency under physiologic conditions.

Correction: Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms.

Correction for 'Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms' by Adam H. Agbaria et al., Analyst, 2020, DOI: 10.1039/D0AN00752H.

Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms.

Physicians diagnose subjectively the etiology of inaccessible infections where sampling is not feasible (such as, pneumonia, sinusitis, cholecystitis, peritonitis), as bacterial or viral. The diagnosis is based on their experience with some medical markers like blood counts and medical symptoms since it is harder to obtain swabs and reliable laboratory results for most cases. In this study, infrared spectroscopy with machine learning algorithms was used for the rapid and objective diagnosis of the etiology of inaccessible infections and enables an assessment of the error for the subjective diagnosis of the etiology of these infections by physicians. Our approach allows for diagnoses of the etiology of both accessible and inaccessible infections as based on an analysis of the innate immune system response through infrared spectroscopy measurements of white blood cell (WBC) samples. In the present study, we examined 343 individuals involving 113 controls, 89 inaccessible bacterial infections, 54 accessible bacterial infections, 60 inaccessible viral infections, and 27 accessible viral infections. Using our approach, the results show that it is possible to differentiate between controls and infections (combined bacterial and viral) with 95% accuracy, and enabling the diagnosis of the etiology of accessible infections as bacterial or viral with >94% sensitivity and > 90% specificity within one hour after the collection of the blood sample with error rate <6%. Based on our approach, the error rate of the physicians' subjective diagnosis of the etiology of inaccessible infections was found to be >23%.

Treatment of transfusion-dependent congenital dyserythropoietic anemia Type I patients with pegylated interferon alpha-2a.

OBJECTIVE: Pegylated IFN-α2a has been reported in two case reports as being efficacious in treating CDA-I patients. This study aims to assess its efficacy on a series of CDA-I patients. METHODS: Study sample consisted of seven CDA type 1 transfusion-dependent patients. They received pegylated interferon alpha-2a at an initial dose of 90-180 µg once a week, tapered according to clinical response and side effects. Good response was defined as Hb ≥ 10 g/dL for ≥3 months, partial response was defined as 7 ≤ Hb<10 g/dL for ≥3 months, and no response was defined as HB < 7 g/dL for over 3 months on treatment. Time to response was defined as the time needed to achieve hemoglobin levels ≥ 10 g/dL without transfusion. Patients were evaluated periodically by abdominal ultrasounds to rule out liver adenomas. RESULTS: Five patients (71%) had a good response to treatment. One patient stopped treatment due to side effects. One patient had partial response. One patient, with more severe phenotype and poor compliance, had poor response to treatment. No abnormal findings were found in ultrasound examination. No effect on serum ferritin level could be established. CONCLUSION: Pegylated interferon α2a therapy is efficacious in CDA-I patients with a reasonable safety profile.