[The application of low-dose multi spiral CT chest scan in pneumoconiosis].

OBJECTIVE: To apply low-dose multi spiral computed tomography (MSCT) chest scans in the early diagnosis and differential diagnosis of pneumoconiosis. METHODS: One hundred and twenty dust-exposed volunteers were examined by MSCT chest scans at conventional dose and low dose, and the results of conventional-dose scans were set as the gold standard. Comparative analysis was performed on the major CT findings and quality of post-processing images, including 1.5 mm and 5.0∼10.0 mm thick high -resolution reconstructed images, multiplanar reformat images, and maximum intensity projection images. RESULTS: One hundred and twenty cases of small circular shadows, 36 cases of ribbon shadows in pulmonary parenchyma, 1 case of honeycombing shadow, and 13 cases of big shadows were all showed on low -dose MSCT. But 94 (95.9%) of 98 cases of interlobular septal thickening shadows and 98 (93.3%) of 105 cases of short branched shadows were detected on low-dose MSCT. There were no significant differences in display of the mentioned large and small shadows between low-dose scans and conventional-dose scans (P > 0.05). Eighty-five cases of small airway disease, 8 cases of pulmonary inflammatory lesions, and 47 cases of hilar and mediastinal lymph node swelling were all detected by MSCT. As for the 46 cases of emphysema, 38 (82.6%) were shown. The low-dose MSCT images of 1.5 mm thickness had more artifacts than those of other thickness. The radiation dose of low-dose MSCT was about 1/3-1/5 of that in the conventional-dose MSCT. CONCLUSION: There is no difference in display of pneumoconiosis between low-dose and conventional-dose MSCT chest scans. With lower radiation dose, low-dose MSCT can be applied in the diagnosis of pneumoconiosis.

Suppression of HCN channels in the spinal dorsal horn restores KCC2 expression and attenuates diabetic neuropathic pain.

Previous studies have shown that the hyperpolarized cyclic nucleotide gated (HCN) ion channels in the spinal dorsal horn (SDH) might be involved in the development of diabetic neuropathic pain (DNP). Additionally, other studies have shown that the decreased potassium-chloride cotransporter 2 (KCC2) expression in the SDH promotes pain hypersensitivity. Both HCN channels and KCC2 were highly expressed in spinal substantia gelatinosa neurons. However, whether the K+ efflux induced by the activation of HCN channels in DNP modulate KCC2 function and subsequently affect the role of γ-aminobutyric acid (GABA)/GABA-A receptors of neurons in the SDH remains to be clarified. The purpose of this work was to investigate the underlying mechanisms of KCC2 participating in HCN channels to promote DNP. Here, we found that the analgesic role of HCN channels blocker ZD7288 was associated with the up-regulated KCC2 expression and could be prevented by DIOA, a KCC2 blocker. Furthermore, the level of GABA in DNP rats significantly increased, which was decreased by ZD72288. Moreover, DIOA pretreatment could partly block the inhibitory effect of ZD7288 on the cyclic adenosine monophosphate-protein kinase A (cAMP-PKA) signaling activation of DNP rats. Finally, inhibition of cAMP-PKA signaling alleviated allodynia and elevated KCC2 expression in DNP rats. Altogether, this study reveals that the role of cAMP-PKA signaling-regulated HCN channels in DNP associated with decreased KCC2 expression in the spinal cord and altered GABA nature.

Analysis of urinary C-C motif chemokine ligand 14 (CCL14) and first-generation urinary biomarkers for predicting renal recovery from acute kidney injury: a prospective exploratory study.

BACKGROUND: Acute kidney injury (AKI) is a frequent syndrome in the intensive care unit (ICU). AKI patients with kidney function recovery have better short-term and long-term prognoses compared with those with non-recovery. Numerous studies focus on biomarkers to distinguish them. To better understand the predictive performance of urinary biomarkers of renal recovery in patients with AKI, we evaluated C-C motif chemokine ligand 14 (CCL14) and two first-generation biomarkers (cell cycle arrest biomarkers and neutrophil gelatinase-associated lipocalin) in two ICU settings. METHODS: We performed a prospective study to analyze urinary biomarkers for predicting renal recovery from AKI. Patients who developed AKI after ICU admission were enrolled and urinary biomarkers including tissue inhibitor of metalloproteinase-2 (TIMP-2), insulin-like growth factor-binding protein 7 (IGFBP7), CCL14, and neutrophil gelatinase-associated lipocalin (NGAL) were detected on the day of AKI diagnosis. The primary endpoint was non-recovery from AKI within 7 days. The individual discriminative ability of CCL14, [TIMP-2] × [IGFBP7] and NGAL to predict renal non-recovery were evaluated by the area under receiver operating characteristics curve (AUC). RESULTS: Of 164 AKI patients, 64 (39.0%) failed to recover from AKI onset. CCL14 showed a fair prediction ability for renal non-recovery with an AUC of 0.71 (95% CI 0.63-0.77, p < 0.001). [TIMP-2] × [IGFBP7] showed the best prediction for renal non-recovery with an AUC of 0.78 (95% CI 0.71-0.84, p < 0.001). However, NGAL had no use in predicting non-recovery with an AUC of 0.53 (95% CI 0.45-0.60, p = 0.562). A two-parameter model (non-renal SOFA score and AKI stage) predicted renal non-recovery with an AUC of 0.77 (95% CI 0.77-0.83, p = 0.004). When [TIMP-2] × [IGFBP7] was combined with the clinical factors, the AUC was significantly improved to 0.82 (95% CI 0.74-0.87, p = 0.049). CONCLUSIONS: Urinary CCL14 and [TIMP-2] × [IGFBP7] were fair predictors of renal non-recovery from AKI. Combing urinary [TIMP-2] × [IGFBP7] with a clinical model consisting of non-renal SOFA score and AKI stage enhanced the predictive power for renal non-recovery. Urinary CCL14 showed no significant advantage in predicting renal non-recovery compared to [TIMP-2] × [IGFBP7].

Chinese experts' consensus on the application of intensive care big data.

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Cell cycle arrest biomarkers for predicting renal recovery from acute kidney injury: a prospective validation study.

BACKGROUND: Acute kidney injury (AKI) is a common disease in the intensive care unit (ICU). AKI patients with nonrecovery of renal function have a markedly increased risk of death compared with patients with recovery. The current study aimed to explore and validate the utility of urinary cell cycle arrest biomarkers for predicting nonrecovery in patients who developed AKI after ICU admission. METHODS: We prospectively and consecutively enrolled 379 critically ill patients who developed AKI after admission to the ICU, which were divided into a derivation cohort (194 AKI patients) and a validation cohort (185 AKI patients). The biomarkers of urinary tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) were detected at inclusion immediately after AKI diagnosis (day 0) and 24 h later (day 1). The optimal cut-off values of these biomarkers for predicting nonrecovery were estimated in the derivation cohort, and their predictive accuracy was assessed in the validation cohort. The primary endpoint was nonrecovery from AKI (within 7 days). RESULTS: Of 379 patients, 159 (41.9%) patients failed to recover from AKI onset, with 79 in the derivation cohort and 80 in the validation cohort. Urinary [TIMP-2]*[IGFBP7] on day 0 showed a better prediction ability for nonrecovery than TIMP-2 and IGFBP7 alone, with an area under the reciever operating characteristic curve (AUC) of 0.751 [95% confidence interval (CI) 0.701-0.852, p < 0.001] and an optimal cut-off value of 1.05 ((ng/mL)2/1000). When [TIMP-2]*[IGFBP7] was combined with the clinical factors of AKI diagnosed by the urine output (UO) criteria, AKI stage 2-3 and nonrenal SOFA score for predicting nonrecovery, the AUC was significantly improved to 0.852 (95% CI 0.750-0.891, p < 0.001), which achieved a sensitivity and specificity of 88.8% (72.9, 98.7) and 92.6% (80.8, 100.0), respectively. However, urine [TIMP-2]*[IGFBP7], TIMP-2 alone, and IGFBP7 alone on day 1 performed poorly for predicting AKI recovery. CONCLUSION: Urinary [TIMP-2]*[IGFBP7] on day 0 showed a fair performance for predicting nonrecovery from AKI. The predictive accuracy can be improved when urinary [TIMP-2]*[IGFBP7] is combined with the clinical factors of AKI diagnosed by the UO criteria, AKI stage 2-3 and nonrenal SOFA score.