Case report: Curing a rare, unstable hemoglobin variant Hb Bristol-Alesha using haploidentical hematopoietic stem cell transplantation.

Unstable hemoglobinopathies are a rare, heterogeneous group of diseases that disrupt the stability of hemoglobin (Hb), leading to chronic hemolysis and anemia. Patients with severe phenotypes often require regular blood transfusions and iron chelation therapy. Although rare, studies have reported that hematopoietic stem cell transplantation (HSCT) seems to be an available curative approach in transfusion-dependent patients with unstable hemoglobinopathies. Here, we describe successful haploidentical HSCT for the treatment of an unstable Hb variant, Hb Bristol-Alesha, in a 6-year-old boy with severe anemia since early childhood. Two years after transplantation, he had a nearly normal hemoglobin level without evidence of hemolysis. DNA analysis showed complete chimerism of the donor cell origin, confirming full engraftment with normal erythropoiesis.

Underwater single-channel acoustic signal multitarget recognition using convolutional neural networks.

The radiated noise from ships is of great significance to target recognition, and several deep learning methods have been developed for the recognition of underwater acoustic signals. Previous studies have focused on single-target recognition, with relatively few reports on multitarget recognition. This paper proposes a deep learning-based single-channel multitarget underwater acoustic signal recognition method for an unknown number of targets in the specified category. The proposed method allows the two subproblems of recognizing the unique class and duplicate categories of multiple targets to be solved. These two tasks are essentially multilabel binary classification and multilabel multiple value classification, respectively. In this paper, we describe the use of real-valued and complex-valued ResNet and DenseNet convolutional networks to recognize synthetic mixed multitarget signals, which was superimposed from individual target signals. We compare the performance of various features, including the original audio signal, complex-valued short-time Fourier transform (STFT) spectrum, magnitude STFT spectrum, logarithmic mel spectrum, and mel frequency cepstral coefficients. The experimental results show that our method can effectively recognize synthetic multitarget ship signals when the magnitude STFT spectrum, complex-valued STFT spectrum, and log-mel spectrum are used as network inputs.

A nomogram for decision-making assistance on surgical treatment of chronic osteomyelitis in long bones: Establishment and validation based on a retrospective multicenter cohort.

BACKGROUND: Chronic osteomyelitis remains a major challenge for orthopedic surgeons due to its high recurrence rate. Surgeons currently have few tools to estimate the likelihood of individual recurrence. We here aimed to develop a nomogram to better estimate individual recurrence rate after surgical treatment of chronic osteomyelitis in long bones. METHODS: We first retrospectively identified patients as training cohort who had received surgical treatment of chronic osteomyelitis in long bones between January 2010 and January 2016 from four hospitals. Patient demographic, microbiological, clinical, and therapeutic variables were collected and analyzed. Univariate and multivariate analyses were performed successively to identify independently predictive factors for recurrence. To reduce overfitting, the Bayesian information criterion was used to reduce variables in the original model. Nomograms were created with the reduced model after model selection. The nomogram was then internally validated with bootstrap resampling. We then further validated the performance of the established nomogram in validation cohort (data from two distinct institutions). RESULTS: Recurrence was found in 136 of 655 (20.8%) and 52 of 201 patients (25.9%) in training and validation cohorts respectively. We included six independent prognostic factors for recurrence in our prediction model: number of previous recurrences, epiphysial involvement, preoperative serum albumin level, axial length of the infectious lesion, lesion-removal method, and application of a muscular flap. After incorporating these six factors, the nomogram achieved good discrimination, with concordance indexes of 0.82 (95% CI, 0.79-0.85) and 0.80 (95% CI, 0.78-0.83) in predicting recurrence in the training and validation cohorts, respectively. Calibration curves were well fitted for both training and validation cohorts. CONCLUSIONS: Our nomogram achieved good preoperative prediction of recurrence in chronic osteomyelitis of long bones. Using this nomogram, the recurrence risk can be confidently predicted for each patient and treatment plan. After considering and discussing the functional prognosis with patients, physicians can establish a rational therapeutic plan. LEVEL OF EVIDENCE: Prognostic, Level III.