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      Miliary TB and COVID-19 Coinfection in a Patient With a History of Post-polycythemia Vera Myelofibrosis Treated With Ruxolitinib: A Case Report

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          Abstract

          The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted the diagnosis and management of tuberculosis (TB), a major public health issue. This case report discusses a 70-year-old female with post-polycythemia vera myelofibrosis (post-PV MF) treated with ruxolitinib who developed miliary TB amidst a COVID-19 infection. The patient presented with a flu-like syndrome over the past week with fatigue and weight loss the last month. When she was admitted to the hospital, the real-time polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was positive. Despite the typical COVID-19 presentation, her clinical and radiographic features raised suspicion for disseminated TB. Diagnostic tests, including bronchoscopy and PCR for Mycobacterium tuberculosis, confirmed miliary TB. She was treated with a standard antitubercular regimen, leading to symptomatic improvement. The interplay between COVID-19 and TB is complex, with COVID-19-induced immunosuppression, particularly lymphocytopenia, facilitating TB reactivation. Additionally, ruxolitinib, a Janus kinase (JAK) inhibitor used for myelofibrosis, impairs immune defense mechanisms, increasing infection risk, including TB. Prompt and accurate diagnosis of TB in the context of COVID-19 is crucial for effective management and improved patient outcomes. Clinicians should remain vigilant for TB reactivation in patients undergoing treatments such as ruxolitinib and consider alternative diagnoses despite positive SARS-CoV-2 tests. This report highlights the necessity for a comprehensive evaluation and timely intervention to mitigate the compounded risks of COVID-19 and TB.

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          Dysregulation of immune response in patients with COVID-19 in Wuhan, China

          Abstract Background In December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and rapidly spread throughout China. Methods Demographic and clinical data of all confirmed cases with COVID-19 on admission at Tongji Hospital from January 10 to February 12, 2020, were collected and analyzed. The data of laboratory examinations, including peripheral lymphocyte subsets, were analyzed and compared between severe and non-severe patients. Results Of the 452 patients with COVID-19 recruited, 286 were diagnosed as severe infection. The median age was 58 years and 235 were male. The most common symptoms were fever, shortness of breath, expectoration, fatigue, dry cough and myalgia. Severe cases tend to have lower lymphocytes counts, higher leukocytes counts and neutrophil-lymphocyte-ratio (NLR), as well as lower percentages of monocytes, eosinophils, and basophils. Most of severe cases demonstrated elevated levels of infection-related biomarkers and inflammatory cytokines. The number of T cells significantly decreased, and more hampered in severe cases. Both helper T cells and suppressor T cells in patients with COVID-19 were below normal levels, and lower level of helper T cells in severe group. The percentage of naïve helper T cells increased and memory helper T cells decreased in severe cases. Patients with COVID-19 also have lower level of regulatory T cells, and more obviously damaged in severe cases. Conclusions The novel coronavirus might mainly act on lymphocytes, especially T lymphocytes. Surveillance of NLR and lymphocyte subsets is helpful in the early screening of critical illness, diagnosis and treatment of COVID-19.
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            Risk Factors for Tuberculosis

            The risk of progression from exposure to the tuberculosis bacilli to the development of active disease is a two-stage process governed by both exogenous and endogenous risk factors. Exogenous factors play a key role in accentuating the progression from exposure to infection among which the bacillary load in the sputum and the proximity of an individual to an infectious TB case are key factors. Similarly endogenous factors lead in progression from infection to active TB disease. Along with well-established risk factors (such as human immunodeficiency virus (HIV), malnutrition, and young age), emerging variables such as diabetes, indoor air pollution, alcohol, use of immunosuppressive drugs, and tobacco smoke play a significant role at both the individual and population level. Socioeconomic and behavioral factors are also shown to increase the susceptibility to infection. Specific groups such as health care workers and indigenous population are also at an increased risk of TB infection and disease. This paper summarizes these factors along with health system issues such as the effects of delay in diagnosis of TB in the transmission of the bacilli.
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              Lymphocyte subset (CD4+, CD8+) counts reflect the severity of infection and predict the clinical outcomes in patients with COVID-19

              Dear Editor, We read with interest the recent article by Chen J et al., which found lower CD4+ T cells count was associated with ICU admission in patients with the coronavirus disease 2019 (COVID-19). 1 Among the clinical and laboratory features of COVID-19, a number of abnormalities have been observed and described, the most prominent of which is total lymphopenia. Through routine blood analysis, a significant reduction in lymphocytes is frequently observed; however, there still lacks thoroughly research about the lymphocyte subset counts. Here, we aimed to investigate the changes of lymphocyte subset counts in COVID-19 patients and determine if these changes are associated with disease severity and prognosis. This retrospective, single-center study was approved by the institutional ethics board of Zhongnan Hospital of Wuhan University, Wuhan, China (No. 2020015). A total of 39 RT-PCR-confirmed COVID-19 patients (admission date from January 18 to February 10, 2020) were enrolled, whose lymphocyte subset counts were tested on admission. The follow-up period lasted till Feb 19, 2020. We obtained demographic and clinical characteristics and laboratory test results from the electronic medical record system. All data were typed in a pre-designed data collection form and checked to verify data accuracy. Continuous and categorical variables were directly expressed as the median (interquartile range (IQR)) and number (%), respectively. The Mann-Whitney U test was used to compare the lymphocyte subset absolute counts of different conditions and outcomes. All statistical analyses were performed using SPSS, version 22.0 (IBM Corp., Armonk, NY). The demographic and clinical characteristics of the patients enrolled are listed in Table 1 . The median age of the patients was 53 years (IQR, 41-61), and they included 20 women and 19 men. The median of the time from onset to admission was 5 days (IQR, 3-7). On admission, leucocyte counts below the normal range appeared in 13 (34.2%) patients, and most patients had leucocyte counts within the normal range. Lymphocyte counts were below the normal range in 29 (76.3%) patients, and no patients showed an increase. The median of lymphocyte count was 0.73 × 10⁹/L (IQR, 0.56-1.07). Lymphocyte subset counts of all subsets decreased in more than half of the patients on admission. T cells decreased in 24 (61.5%) patients, CD4+ T cells decreased in 22 (56.4%) patients, CD8+ T cells decreased in 28 (71.8%) patients, B cells decreased in 27 (69.2%) patients, and NK cells decreased in 30 (76.9%) patients. No patient had subsets increased. Among patients enrolled, the median time of onset to RT-PCR turning negative was 14 (IQR, 10-20) days. Table 1 Demographic and clinical characteristics of 39 patients with COVID-19. Table 1: Characteristics (normal range) Median (IQR) / N (%) Increased, N (%) Decreased, N (%) Age, year 53 (41-61) - - Sex  Female 20 (51.3) - -  Male 19 (48.7) - - Onset to admission, d 5 (3-7) - - Leucocytes (3.5-9.5 × 10⁹/L) 4.11 (3.33-5.16) 13 (34.2) 3 (7.9) Lymphocytes (1.1-3.2 × 10⁹/L) 0.73 (0.56-1.07) 0 29 (76.3) T cells (805-4459 × 106/L) 561.0 (300.0-1056.0) 0 24 (61.5) CD4+ T cells (345-2350 × 106/L) 308.0 (176.0-665.0) 0 22 (56.4) CD8+ T cells (345-2350 × 106/L) 168.0 (117.0-368.0) 0 28 (71.8) CD4+/CD8+ (0.96-2.05) 1.640 (1.140-2.380) 14 (35.9) 7 (17.9) B cells (240-1317 × 106/L) 146.0 (56.0-272.0) 0 27 (69.2) NK cells (210-1514 × 106/L) 136.0 (57.0-207.0) 0 30 (76.9) Onset to RT-PCR turning negative, d 14 (10-20) - - Among the 39 patients, 15 (38.5%) had comorbidities, and 17 (43.6%) spent time from onset to admission over 5 days. According to the Guidelines for the Diagnosis and Treatment of COVID-19 (Trial Version 6), 2 21 (53.8%) patients were classified as having mild and moderate infection, 18 (46.2%) had severe and critical infection. Half of the patients (20 [51.3%]) had a negative RT-PCR result within 14 days after onset. Until the end of follow-up, 16 (43.2%) patients were given an outcome of continual cure or death. T cells, CD4+ T cells, and CD8+ T cells were all statistically higher in patients who had a mild infection, timely hospitalization, and fast recovery (all p 0.05). All analyses indicated that changes in NK cell counts and the ratio of CD4+ T cells to CD8+ T cells (CD4+/ CD8+) were not different between different conditions and outcomes. These finding are shown in Table 2 . Table 2 Lymphocyte subset counts of 39 patients with COVID-19 in different conditions and outcomes. Table 2: T cells, × 106/L CD4+ T cells, × 106/L CD8+ T cells, × 106/L CD4+ / CD8+ B cells, × 106/L NK cells, × 106/L Comorbidities No (n=24, 61.5%) 770.5 (362.0-1151.3) 476.0 (182.3-697.8) 261.0 (139.0-554.5) 1.400 (0.975-2.383) 196.5 (87.3-286.5) 147.0 (60.8-227.3) Yes (n=15, 38.5%) 375.0 (203.0-914.0) 252.0 (126.0-424.0) 142.0 (76.0-337.0) 1.870 (1.430-2.380) 91.0 (44.0-185.0) 136.0 (33.0-164.0) P value 0.069 0.133 0.046* 0.225 0.081 0.419 Onset to admission (d) ≤5 (n=22, 56.4%) 896.0 (484.5-1216.5) 530.0 (278.0-712.8) 312.5 (158.5-619.0) 1.645(1.065-2.275) 167.0 (53.5-285.3) 156.5 (45.0-254.5) >5 (n=17, 43.6%) 362.0 (258.5-605.0) 221.0 (128.0-305.5) 137.0 (88.5-205.5) 1.480 (1.160-2.490) 119.0 (69.5-232.5) 136.0 (63.0-152.0) P value 0.008* 0.017* 0.016* 0.843 0.453 0.295 Imaging changes Improvement (n=8, 25.8%) 569.5 (223.0-1020.5) 295.0 (138.5-608.5) 229.5 (81.0-354.3) 1.800 (0.960-2.180) 203.5 (59.0-286.5) 123.5 (42.0-157.8) Progression (n=23, 74.2%) 501.0 (266.0-878.0) 290.0 (148.0-630.0) 164.0 (81.0-288.0) 1.780 (1.260-2.530) 110.0 (55.0-246.0) 107.0 (31.0-192.0) P value 0.857 1.000 0.786 0.470 0.470 1.000 Severity degree Mild or moderate (n=21, 53.8%) 914.0 (468.0-1214.0) 591.0 (266.0-718.5) 288.0 (165.0-414.5) 1.780 (1.305-2.330) 174.0 (69.5-306.5) 149.0 (58.5-240.5) Severe and critical (n=18, 46.2%) 343.5(237.0-730.3) 217.5 (112.8-324.5) 122.5(76.0-256.8) 1.345 (0.930-2.413) 105.0 (55.8-235.5) 123.5(44.5-177.8) P value 0.004* 0.006* 0.011* 0.447 0.360 0.352 Oxygen therapy No (n=23, 59.0%) 896.0 (484.5-1114.0) 530.0 (278.0-679.3) 312.5 (165.5-414.3) 1.715 (1.065-2.275) 207.0 (79.8-298.8) 142.5 (58.8-217.5) Yes (n=16, 41.0%) 325.0 (223.0-605.0) 201.0 (109.5-305.5) 122.0 (74.0-191.5) 1.440 (1.200-2.445) 91.0 (55.5-190.5) 136.0 (40.0-183.0) P value 0.004* 0.013* 0.006* 0.955 0.145 0.571 Onset to RT-P CR turning negative (d) ≤14 (n=20, 51.3%) 896.0 (361.3-1156.0) 530.0 (248.3-715.0) 337.5(151.0-414.8) 1.800 (1.285-2.583) 230.5(94.3-363.0) 130.5(39.0-210.8) >14 (n=19, 48.7%) 426.0 (195.0-719.0) 242.0 (103.0-320.0) 142.0 (96.0-273.0) 1.440 (1.080-2.060) 90.0 (49.0-174.0) 136.0 (59.0-192.0) P value 0.019* 0.011* 0.046* 0.164 0.032* 1.000 Clinical outcome Hospital discharge (n=21, 56.8%) 878.0 (362.0-1256.0) 518.0 (208.0-700.0) 338.0 (154.0-511.5) 1.460 (0.980-2.335) 185.0 (72.0-282.0) 139.0 (45.5-243.5) Continual cure or death (n=16, 43.2%) 375.5 (215.0-664.5) 260.0 (129.5-317.0) 130.0 (70.0-182.0) 1.825 (1.200-2.425) 90.5 (50.5-204.3) 101.0 (51.0-160.3) P value 0.014* 0.046* 0.005* 0.399 0.098 0.350 Note: ⁎ represent the P value <0.05. Counts of lymphocyte and lymphocyte subset are of great value to ensure immune system functionality. Viral infections, immunodeficiency diseases, and other infectious diseases usually lead to abnormal changes in the levels of lymphocyte subsets. 3, 4, 5 In this study, we focused on the abnormal counts of lymphocyte and lymphocyte subset. This result is quite different from that associated with pneumonia caused by common respiratory viruses, such as respiratory syncytial virus, which is typically associated with a normal or elevated lymphocyte count. 6 Here, we found that the CD4+ T cell and CD8+ T cell counts were closely related to disease severity and clinical outcome when we compared the counts of lymphocyte subsets in different patient groups. The more serious the disease and the worse the prognosis, the lower were the T cell, CD4+ T cell, and CD8+ T cell counts on admission. Based on these findings, we believe that the CD4+ and CD8+ T cell counts in patients with COVID-9 could reflect disease severity and predict disease prognosis and are therefore good biomarkers of COVID-19 activity. In conclusion, lymphocyte subset (CD4+ and CD8+ T cell) counts reflected the disease severity and associated with clinical outcomes, which can be considered as good biomarkers of COVID-19. Early hospitalization influenced the level of CD4+ and CD8+ T cells so patients required a quick hospitalization. If patients have relatively low counts of CD4+ and CD8+ T cell on admission, they may be in a relatively severe infection with the SARS-CoV-2 virus and come to a worse prognosis. These patients should gain more attention to the change of their illness severity. Author Contributions LG and ZL contributed conception and design of the study; YH, SW, SC and MT contributed to data acquisition and check; WL performed the statistical analysis and wrote the first draft of the manuscript; WZ, LZ, MW and DC revised the tables; MW, QH, HX and WZ contributed to manuscript revision. All authors contributed to data interpretation and approved the final version. Funding sourses This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Competing Interest None.
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                Author and article information

                Journal
                Cureus
                Cureus
                2168-8184
                Cureus
                Cureus (Palo Alto (CA) )
                2168-8184
                3 July 2024
                July 2024
                : 16
                : 7
                : e63791
                Affiliations
                [1 ] Department of Respiratory Medicine, Medical School, Democritus University of Thrace, Alexandroupolis, GRC
                [2 ] Department of Pathophysiology/Pulmonology, Laiko General Hospital, Athens, GRC
                Author notes
                Vasiliki E. Georgakopoulou vaso_georgakopoulou@ 123456hotmail.com
                Article
                10.7759/cureus.63791
                11297589
                39100065
                c02d321a-e29d-4d73-be5d-23eb518a2a0e
                Copyright © 2024, Loutsou et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 July 2024
                Categories
                Infectious Disease
                Pulmonology
                Hematology

                post-polycythemia vera myelofibrosis,immunosuppression,ruxolitinib,covid-19 coinfection,miliary tuberculosis

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