Withdrawal from Hemodialysis in a Patient with IgD Type Multiple Myeloma: A Case-based Review.

Several previous case reports have shown that patients with immunoglobulin D (IgD) multiple myeloma (MM) can be withdrawn from hemodialysis, however, the characteristics that can predict withdrawal in these patients have not yet been elucidated. A 57-year-old Japanese woman required hemodialysis because of renal dysfunction due to IgD-λ and Bence Jones protein-λ MM. Bortezomib-based chemotherapy nine days after admission led to her withdrawal from hemodialysis on Day 50. In our case-based review, younger age and early initiation of bortezomib-based chemotherapy emerged as possible predictors of successful hemodialysis withdrawal.

Bicarbonate concentration as a predictor of prognosis in moderately severe COVID-19 patients: A multicenter retrospective study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) patients reportedly have high bicarbonate concentration. However, its relationship to the disease progression are obscure. METHODS: In this two-center retrospective study, we included COVID-19 patients with moderate severity between March 2020 and May 2021. We classified patients into three groups according to bicarbonate concentrations: high (>27 mEq/L), normal (21 to 27 mEq/L), and low (<21 mEq/L). The primary outcome was the time to clinical worsening defined by the requirement of intubation or death during 90 days. We evaluated high or low bicarbonate concentration during the clinical course related to the primary outcome using multivariable Cox proportional hazard models. RESULTS: Of the 60 participants (median age 72 years), 60% were men. Participants were classified into high (13 patients), normal (30 patients), and low (17 patients) groups. Clinical worsening occurred in 54% of patients in the high group, 23% in the normal group, and 65% in the low group. Both high and low groups were associated with a higher clinical worsening rate: HR, 3.02 (95% CI, 1.05 to 8.63) in the high group; 3.49 (95% CI: 1.33 to 9.12) in the low group. CONCLUSION: Monitoring of bicarbonate concentrations may be useful to predict the prognosis.

Suspected Transmission of Severe Fever with Thrombocytopenia Syndrome Virus from a Cat to a Veterinarian by a Single Contact: A Case Report.

A 67-year-old male veterinarian presented with fatigue, anorexia, and diarrhea. Although there were no tick bite marks, we suspected severe fever with thrombocytopenia syndrome (SFTS) due to bicytopenia, mild disturbance of consciousness, and a history of outdoor activities. Thus, we started immunoglobulin therapy immediately. A serum reverse transcription-polymerase chain reaction (RT-PCR) test for SFTS virus (SFTSV) was positive. The patient had treated a cat with thrombocytopenia 10 days prior to admission. The cat's serum SFTSV RT-PCR test result was positive, and the whole genome sequences of the patient's and cat's SFTSV were identical, suggesting the possibility of transmission from the cat to the patient. Other cases of direct cat-to-human SFTV transmission have been reported recently. Mucous membranes should be protected, including eye protection, in addition to standard precautions, when in contact with any cat with suspected SFTS.

Added Diagnostic Value of Cerebrospinal Fluid Carcinoembryonic Antigen in a Patient with Leptomeningeal Carcinomatosis as the Initial Manifestation of Gastric Cancer.

A 77-year-old woman with no history of malignancy presented with anorexia and bilateral lower extremity weakness. Her consciousness level worsened daily, so we performed a lumbar puncture. Cerebrospinal fluid (CSF) analysis indicated meningitis, but three rounds of CSF cytology showed no malignant cells. The patient's carcinoembryonic antigen (CEA) level was highly elevated in CSF, but normal in serum. Through gadolinium-enhanced brain/spinal magnetic resonance imaging and gastrointestinal endoscopy, she was diagnosed with leptomeningeal carcinomatosis (LC) from gastric cancer. CEA level in CSF facilitated the diagnosis of LC from gastric cancer because there were no malignant cells on CSF cytology.

Recurrent atelectasis and brain infarction in a patient with anti-neutrophil antibody negative eosinophilic granulomatosis with polyangiitis: a case report.

BACKGROUND: Eosinophilic granulomatosis with polyangiitis (EGPA) is an anti-neutrophil antibody (ANCA)-associated necrotizing vasculitis, which predominantly affects small to medium vessels, and is associated with asthma and eosinophilia. EGPA has two different pathogenic aspects: eosinophilic granulomatous inflammation and ANCA-associated inflammation. A recent histological study of peripheral nerves showed that not only ANCA-associated inflammation but also eosinophil-associated vascular occlusion leads to ischemia. Endobronchial involvement is relatively common especially in the patients with granulomatosis with polyangiitis but rare in patients with EGPA. Central nervous system (CNS) involvement is also rare in patients with EGPA, the pathogenesis and relationship between these two rare conditions have not been elucidated. CASE PRESENTATION: A 62-year-old woman was admitted with numbness, purpura, and eosinophilia. She had a 3-year-history of bronchial asthma. Chest computed tomography showed left lower lobe collapse, and brain magnetic resonance imaging indicated occipital lobe infarction. Skin biopsy findings led to the diagnosis of EGPA. ANCA test results were negative. All symptoms improved after initiating glucocorticoids. However, atelectasis and brain infarction relapsed with increasing eosinophil counts. Atelectasis quickly disappeared with increasing glucocorticoid dose, and glucocorticoid could be reduced to a maintenance dose after the initiation of mepolizumab. CONCLUSION: Both atelectasis and brain infarction might develop not only via ANCA-associated inflammation but also via eosinophilic inflammation.

Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback.

We study the problem of stochastic multiple-arm identification, where an agent sequentially explores a size-k subset of arms (also known as a super arm) from given n arms and tries to identify the best super arm. Most work so far has considered the semi-bandit setting, where the agent can observe the reward of each pulled arm or assumed each arm can be queried at each round. However, in real-world applications, it is costly or sometimes impossible to observe a reward of individual arms. In this study, we tackle the full-bandit setting, where only a noisy observation of the total sum of a super arm is given at each pull. Although our problem can be regarded as an instance of the best arm identification in linear bandits, a naive approach based on linear bandits is computationally infeasible since the number of super arms K is exponential. To cope with this problem, we first design a polynomial-time approximation algorithm for a 0-1 quadratic programming problem arising in confidence ellipsoid maximization. Based on our approximation algorithm, we propose a bandit algorithm whose computation time is O(log K), thereby achieving an exponential speedup over linear bandit algorithms. We provide a sample complexity upper bound that is still worst-case optimal. Finally, we conduct experiments on large-scale data sets with more than 1010 super arms, demonstrating the superiority of our algorithms in terms of both the computation time and the sample complexity.

pSpatiocyte: a high-performance simulator for intracellular reaction-diffusion systems.

BACKGROUND: Studies using quantitative experimental methods have shown that intracellular spatial distribution of molecules plays a central role in many cellular systems. Spatially resolved computer simulations can integrate quantitative data from these experiments to construct physically accurate models of the systems. Although computationally expensive, microscopic resolution reaction-diffusion simulators, such as Spatiocyte can directly capture intracellular effects comprising diffusion-limited reactions and volume exclusion from crowded molecules by explicitly representing individual diffusing molecules in space. To alleviate the steep computational cost typically associated with the simulation of large or crowded intracellular compartments, we present a parallelized Spatiocyte method called pSpatiocyte. RESULTS: The new high-performance method employs unique parallelization schemes on hexagonal close-packed (HCP) lattice to efficiently exploit the resources of common workstations and large distributed memory parallel computers. We introduce a coordinate system for fast accesses to HCP lattice voxels, a parallelized event scheduler, a parallelized Gillespie's direct-method for unimolecular reactions, and a parallelized event for diffusion and bimolecular reaction processes. We verified the correctness of pSpatiocyte reaction and diffusion processes by comparison to theory. To evaluate the performance of pSpatiocyte, we performed a series of parallelized diffusion runs on the RIKEN K computer. In the case of fine lattice discretization with low voxel occupancy, pSpatiocyte exhibited 74% parallel efficiency and achieved a speedup of 7686 times with 663552 cores compared to the runtime with 64 cores. In the weak scaling performance, pSpatiocyte obtained efficiencies of at least 60% with up to 663552 cores. When executing the Michaelis-Menten benchmark model on an eight-core workstation, pSpatiocyte required 45- and 55-fold shorter runtimes than Smoldyn and the parallel version of ReaDDy, respectively. As a high-performance application example, we study the dual phosphorylation-dephosphorylation cycle of the MAPK system, a typical reaction network motif in cell signaling pathways. CONCLUSIONS: pSpatiocyte demonstrates good accuracies, fast runtimes and a significant performance advantage over well-known microscopic particle methods in large-scale simulations of intracellular reaction-diffusion systems. The source code of pSpatiocyte is available at https://spatiocyte.org.

Z-Score-Based Modularity for Community Detection in Networks.

Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given partition with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function.