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Osteosclerosis in multiple myeloma (MM) is typically associated with rare POEMS syndrome, characterized by polyneuropathy (P), organomegaly (O), endocrinopathy (E), M-protein (M), and skin changes (S). However, osteosclerosis in multiple myeloma (MM) without POEMS syndrome, defined as non-POEMS Osteosclerotic MM, is exceedingly rare. We report a 70-year-old man with rib pain, remarkably high bone mineral density and diffuse osteosclerosis. The diagnosis of non-POEMS osteosclerotic MM was confirmed by biopsy and aspiration of bone marrow through surgery. A literature review spanning from 1990 identified 12 cases of similar non-POEMS osteosclerotic MM, including 5 males and 7 females with a mean age of 59.7 ± 10.6 years. The non-POEMS osteosclerotic MM can be divided into two subtypes, the osteosclerotic lesion subtype and the diffuse osteosclerosis subtype. Absence of polyneuropathy and organomegaly are the main factors that differentiate non-POEMS osteosclerotic MM from POEMS. A hyperactive osteoblastic process might be the etiology of diffuse osteosclerosis. Further research is needed to understand its etiology and pathophysiology.

Abstract Objective: Previous studies suggest a link between vitamin D status and COVID-19 susceptibility in hospitalised patients. This study aimed to investigate whether vitamin D concentrations in elderly individuals were associated with their susceptibility to Omicron COVID-19 incidence, the severity of the disease and the likelihood of reoccurrence during the era of the post-‘zero-COVID-19’ policies in China. Design: In this retrospective study, participants were categorised into three groups based on their 25(OH)D concentrations: deficiency (< 20 ng/ml), insufficiency (20 to < 30 ng/ml) and sufficiency (≥ 30 ng/ml). The demographic and clinical characteristics, comorbidities and the incidence rate, reoccurrence rate and severity of Omicron COVID-19 were retrospectively recorded and analysed by using hospital information system data and an online questionnaire survey. Setting: China. Participants: 222 participants aged 60 years or older from a health management centre. Results: Our findings revealed significant differences in the incidence (P = 0·03) and recurrent rate (P = 0·02) of Omicron COVID-19 among the three groups. Participants with lower 25(OH)D concentrations (< 20 ng/ml) exhibited higher rates of initial incidence and reoccurrence and a greater percentage of severe and critical cases. Conversely, individuals with 25(OH)D concentrations ≥ 30 ng/ml had a higher percentage of mild cases (P = 0·003). Binary and ordinal logistic regression models indicated that vitamin D supplementation was not a significant risk factor for COVID-19 outcomes. Conclusions: In the elderly population, pre-infection vitamin D deficiency was associated with increased susceptibility to incidence, severity of illness and reoccurrence rates of Omicron COVID-19.

Background: Multi-wavelength transmittance spectroscopy, in combination with the artificial neural network, has been a novel tool used to identify and classify microorganisms in recent years.Methods: In our work, the transmittance spectra in the region from 200 to 900 nm for four bacterial species of interest, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), Klebsiella pneumoniae (K.pneumoniae), and Salmonella typhimurium (S. typhi), were recorded using an ultraviolet–visible spectrophotometer. Considering too much redundant data on the full-wave band spectra, the characteristic wavelength variables were selected using the competitive adaptive reweighting sampling (CARS) algorithm. Spectra of the initial training set of these targeted microorganisms were used to create identification models representing the spectral variability of each species using four kinds of neural networks, namely, backpropagation (BP), radial basis function network (RBF), generalized regression neural network (GRNN), and probabilistic neural network (PNN).Results: The blinded isolate spectra of targeted species were identified using the four identification models given above. Compared to fullband modeling, after using CARS to screen the wavelength variables, four identification models are established for the 35 preferred characteristic wavelengths, and the prediction performance of the four models is notably improved. Among them, the CARS–PNN model is the best, and the identification rates of all targeted bacteria were achieved with 100% accuracy; the calculation time is just approximately 0.04 s.Discussion: The use of CARS can effectively remove useless information from the spectra, reduce model complexity, and enhance model prediction performance. Multi-wavelength transmission spectroscopy, combined with the CARS–PNN method, can provide a new method for the rapid detection of bacteria in water and could be readily extended for bacterial microbiological detection in blood and food.

Objectives: This study assessed the efficacy, safety, pharmacokinetics (PK), and immunogenicity profiles of a denosumab biosimilar (LY06006) in Chinese postmenopausal osteoporotic women with a high risk of fracture. Methods: In this multicenter, randomized, double-blind, placebo-controlled, phase 3 trial, 448 postmenopausal women aged 50–85 years with osteoporosis were enrolled at 49 centers in China and were randomly assigned (3:1) to receive 60 ​mg of the denosumab biosimilar (LY06006) or placebo subcutaneously every 6 months for 1 year. Lumbar spine bone mineral density (BMD) change was the primary endpoint. Results: Of the 448 randomized patients, 409 (LY06006, n ​= ​311; placebo, n ​= ​98) completed the study. All 448 (100.0%) subjects were included in the intent-to-treat (ITT) trial, 427 (95.3%) were included in the full analysis set (FAS), 408 (91.1%) were included in the per protocol set (PPS), 446 (99.6%) were included in the safety set (SS), and 336 (75.0%) were included in the pharmacokinetics concentration set (PKCs). For the primary endpoint, a 4.71% (95% CI, 3.81%, 5.60%) treatment difference in percent change in lumbar spine BMD from baseline to month 12 was observed in the LY06006 group compared with the placebo group (P ​

Abstract Objectives The extension of diffuse idiopathic skeletal hyperostosis (DISH) from the low thoracic spine to the lumbar spine result in adjustment of spinal sagittal alignment in surgical patients. The aim of this study was to investigate changes in sagittal alignment and back pain in the thoracolumbar spine in nonsurgical DISH and control participants selected from a radiological database. Methods Participants in the DISH and the control group were selected by searching for “DISH or degenerative changes in the thoracic spine” in the radiology database of Taizhou Hospital between 2018 and 2021 using Resnick and Niwayama’s criteria. The subjects with spinal tumors, previous spinal surgery, vertebral fractures, inflammatory diseases, poor-quality radiographs, or loss of follow-up were excluded. Demographic and clinical characteristics were recorded retrospectively via the hospital information system and telephone follow-up. Segmental disc angles (SDAs), lumbar lordosis (LL), and bridge scores were analyzed using images of three-dimensional CT. Results The final participants consisted of 51 individuals with DISH (DISH group) and 102 individuals without DISH (control group). Depending on the presence of thoracolumbar pain, the DISH group was divided into the DISH group with thoracolumbar pain (DISH+Pain) and the DISH group without thoracolumbar pain (DISH-Pain). The LL and SDAs of T11-T12 and T12-L1 were significantly greater in the DISH group than in the control group. In addition, the SDA of L1-L2 was significantly smaller in the DISH+Pain group than in the DISH-Pain group, whereas there was no significant difference in lumbar lordosis between the DISH+Pain group and the DISH-Pain group. The bridge scores in DISH+Pain group was larger in T10-T11 (p = 0.01) and L1-L2 (p

Bacterial concentration is an important indicator to measure the degree of water pollution. Realizing rapid and accurate quantification of bacterial concentration in water is of great significance for ensuring water safety and maintaining human health. This paper proposes a method for rapid determination of bacterial concentration by multiwavelength transmission spectroscopy combined with partial least squares regression. Escherichia coli (E. coli) is selected because it is a common indicator microorganism for assessing water pollution status, and it is easy to handle. First, we measure the transmission spectra for E. coli suspensions in the region from 200 to 900 nm and analyze the differences in the spectral characteristics at different concentrations; subsequently, considering that the concentration is affected by the instrument linearity and other factors, the sensitivity, correlation, and detection ability of the spectra at different wavelengths with the change of concentration are analyzed, and the optimal characteristic band is selected according to its wavelength variation characteristics; finally, the determination of E. coli concentrations are completed by using the optimal characteristic band spectra combined with partial least squares regression. We calculate the bacterial concentration, compared with the plate counting, the maximum relative error is 4.500%, the average relative error is 0.677%, respectively, which is less than 5%, and their accuracy and stability are all better than those calculated by the single-wavelength method. This study provides a reference for the rapid and accurate detection of bacterial concentration in water.

Background/Objective: Histology-based analyses are important tools to dissect cellular and molecular mechanisms of skeletal homeostasis, diseases, and regeneration. The success of these efforts is highly dependent on rapidly obtaining high-quality sections of mineralized skeletal tissues suitable for various analyses. However, the current techniques for preparing such sections are still far from satisfactory. This study aimed to develop a new approach for preparing high-quality undecalcified bone sections applicable to various histological analyses. Methods: Two important modifications were made to the conventional Cryojane Tape-Transfer System, including utilization of an optimized adhesive to prepare adhesive glass slides for improving the transfer efficiency, and a cheap conventional benchtop UV transilluminator for UV curing. Cryosections of undecalcified rodent bones were prepared using this modified tape transfer approach, and their tissue morphology and structural integrity were visually examined. A variety of histological analyses, including calcein labeling, Von kossa staining, immunofluorescence, and enzymatic activity staining as well as 5-Ethynyl-2’-deoxyuridine (EdU) and TUNEL assays, were performed on these sections. Results: We developed a modified version of tape transfer approach that can prepare cryosections of undecalcified rodent adult bones within 4 days at a low cost. Bone sections prepared by this approach exhibited good tissue morphology and structural integrity. Moreover, these sections were applicable to a variety of histological analyses, including calcein labeling, Von kossa staining, immunofluorescence, and enzymatic activity staining as well as EdU and TUNEL assays. Conclusion: The tape transfer approach we developed provides a rapid, affordable, and easy learning method for preparing high-quality undecalcified bone sections valuable for bone research. The translational potential of this article: Our research provides a rapid, affordable, and easy learning method for preparing high-quality undecalcified bone sections that can be potentially used for accurate diagnosis of various bone disorders and evaluation of the efficacy of different therapies in the treatment of these diseases.

Media coverage plays an important role in prevention and control the spread of COVID-19 during the pandemic. In this paper, an SIHRS model of COVID-19 pandemic with impulse and time delay under media coverage is established. The positive and negative emotions of public are considered by the impact of confirmed cases and medical resources. In order to restrain the negative information of public, the factor of policies and regulations with impulse and time delay is introduced. Furthermore, the system model is simulated and verified by the reported data of COVID-19 pandemic in Wuhan. The main results are as follows: (1) When the implementation rate of the negative information generated by the confirmed cases gradually reduced to 0.4 times, the cumulative confirmed cases will be significantly reduced to about 37000, indicating that the popularization of pandemic related media information should be broad; (2) When the implementation rate affected by the amount of policies and regulations information gradually increases to 3 times, the cumulative confirmed cases will be significantly reduced to about 28000, indicating that the policies and regulations information should be continuously and incrementally reported; (3) When the inhibition rate of policies and regulation information on negative information gradually increases to 3 times, the cumulative confirmed cases will also be significantly reduced to about 27000 cases, indicating that the targeted policies and regulations information has a significant impact on inhibiting the corresponding negative emotions.

Abstract Background Psoas hematoma rarely occurs in patients with spondylolisthesis who undergo posterior lumbar interbody fusion (PLIF) surgery. Case presentation Here we reported a case of a 57-year-old male patient diagnosed with spondylolisthesis who underwent PLIF at the local hospital. Seven days post-surgery, abdominal pain occurred, and the pain in the right lower limb gradually increased. The computerized tomography (CT) indicated a formation of hematoma around the psoas muscle. Digital-subtraction angiography (DSA) suggested a vascular injury, a rupture of the right segmental artery of the lumbar vertebral level 4. The patient then received DSA vascular embolization, after which the lower lumbar segmental artery active bleeding was stopped. One month after discharge, the abdominal hematoma was gradually absorbed, and the pain in the waist, leg, and abdomen disappeared. Conclusion Symptoms such as abdominal pain, abdominal distension, and exacerbation of lower limb pain, may suggest the occurrence of psoas hematoma after PLIF. DSA vascular embolization is suggested as the first treatment approach for this type of complication.

A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic diagnosis based on machine learning (ML) brings promising solutions. Traditional ML requires complicated preprocessing steps for feature extraction. Its clinical applications are thus limited to particular datasets. Deep learning (DL), which extracts features from convolution layers, can embrace larger datasets that might be deliberately excluded in traditional algorithms. However, DL requires large sets of domain specific labeled data for training. Labeling various tissues of pressure ulcers is a challenge even for experienced plastic surgeons. We propose a superpixel-assisted, region-based method of labeling images for tissue classification. The boundary-based method is applied to create a dataset for wound and re-epithelialization (re-ep) segmentation. Five popular DL models (U-Net, DeeplabV3, PsPNet, FPN, and Mask R-CNN) with encoder (ResNet-101) were trained on the two datasets. A total of 2836 images of pressure ulcers were labeled for tissue classification, while 2893 images were labeled for wound and re-ep segmentation. All five models had satisfactory results. DeeplabV3 had the best performance on both tasks with a precision of 0.9915, recall of 0.9915 and accuracy of 0.9957 on the tissue classification; and a precision of 0.9888, recall of 0.9887 and accuracy of 0.9925 on the wound and re-ep segmentation task. Combining segmentation results with clinical data, our algorithm can detect the signs of wound healing, monitor the progress of healing, estimate the wound size, and suggest the need for surgical debridement.

BackgroundAccurate assessment of the percentage total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancies in estimating %TBSA by different doctors. ObjectiveWe propose a method, based on deep learning, for burn wound detection, segmentation, and calculation of %TBSA on a pixel-to-pixel basis. MethodsA 2-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected from medical records and labeled by burn surgeons, and the data set was then input into 2 deep learning architectures, U-Net and Mask R-CNN, each configured with 2 different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another data set, which was also input into U-Net and Mask R-CNN to segment the hands. The %TBSA of burn wounds was then calculated by comparing the pixels of mask areas on images of the burn wound and hand of the same patient according to the rule of hand, which states that one’s hand accounts for 0.8% of TBSA. ResultsA total of 2591 images of burn wounds were collected and labeled to form the burn wound data set. The data set was randomly split into training, validation, and testing sets in a ratio of 8:1:1. Four hundred images of volar hands were collected and labeled to form the hand data set, which was also split into 3 sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with DC values of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons. ConclusionsThis is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately, thereby helping to provide precise care to burn victims.