Table_1_Development and validation of a novel nomogram for prediction of ketosis-prone type 2 diabetes.xlsx

BackgroundKetosis-prone type 2 diabetes (KPD), as a unique emerging clinical entity, often has no clear inducement or obvious clinical symptoms at the onset of the disease. Failure to determine ketosis in time may lead to more serious consequences and even death. Therefore, our study aimed to develop and validate a novel nomogram to predict KPD.MethodsIn this retrospective study, clinical data of a total of 398 newly diagnosed type 2 diabetes in our hospital who met our research standards with an average age of 48.75 ± 13.86 years years old from January 2019 to December 2022 were collected. According to the occurrence of ketosis, there were divided into T2DM groups(228 cases)with an average age of 52.19 ± 12.97 years, of whom 69.74% were male and KPD groups (170cases)with an average age of 44.13 ± 13.72 years, of whom males account for 80.59%. Univariate and multivariate logistic regression analysis was performed to identify the independent influencing factors of KPD and then a novel prediction nomogram model was established based on these independent predictors visually by using R4.3. Verification and evaluation of predictive model performance comprised receiver-operating characteristic (ROC) curve, corrected calibration curve, and clinical decision curve (DCA).Results4 primary independent predict factors of KPD were identified by univariate and multivariate logistic regression analysis and entered into the nomogram including age, family history, HbA1c and FFA. The model incorporating these 4 predict factors displayed good discrimination to predict KPD with the area under the ROC curve (AUC) of 0.945. The corrected calibration curve of the nomogram showed good fitting ability with an average absolute error =0.006 ConclusionIn our novel prediction nomogram model, we found that age, family history, HbA1c and FFA were the independent predict factors of KPD. The proposed nomogram built by these 4 predictors was well developed and exhibited powerful predictive performance for KPD with high discrimination, good accuracy, and potential clinical applicability, which may be a useful tool for early screening and identification of high-risk population of KPD and therefore help clinicians in making customized treatment strategy.</p

Optical Microfiber Intelligent Sensor: Wearable Cardiorespiratory and Behavior Monitoring with a Flexible Wave-Shaped Polymer Optical Microfiber

With the advantages of high flexibility, strong real-time monitoring capabilities, and convenience, wearable devices have shown increasingly powerful application potential in medical rehabilitation, health monitoring, the Internet of Things, and human–computer interaction. In this paper, we propose a novel and wearable optical microfiber intelligent sensor based on a wavy-shaped polymer optical microfiber (WPOMF) for cardiorespiratory and behavioral monitoring of humans. The optical fibers based on polymer materials are prepared into optical microfibers, fully using the advantages of the polymer material and optical microfibers. The prepared polymer optical microfiber is designed into a flexible wave-shaped structure, which enables the WPOMF sensor to have higher tensile properties and detection sensitivity. Cardiorespiratory and behavioral detection experiments based on the WPOMF sensor are successfully performed, which demonstrates the high sensitivity and stability potential of the WPOMF sensor when performing wearable tasks. Further, the success of the AI-assisted medical keyword pronunciation recognition experiment fully demonstrates the feasibility of integrating AI technology with the WPOMF sensor, which can effectively improve the intelligence of the sensor as a wearable device. As an optical microfiber intelligent sensor, the WPOMF sensor offers broad application prospects in disease monitoring, rehabilitation medicine, the Internet of Things, and other fields

Human paleodiet and animal utilization strategies during the Bronze Age in northwest Yunnan Province, southwest China

<div><p>Reconstructing ancient diets and the use of animals and plants augment our understanding of how humans adapted to different environments. Yunnan Province in southwest China is ecologically and environmentally diverse. During the Neolithic and Bronze Age periods, this region was occupied by a variety of local culture groups with diverse subsistence systems and material culture. In this paper, we obtained carbon (δ¹³C) and nitrogen (δ¹⁵N) isotopic ratios from human and faunal remains in order to reconstruct human paleodiets and strategies for animal exploitation at the Bronze Age site of Shilinggang (ca. 2500 Cal BP) in northwest Yunnan Province. The δ¹³C results for human samples from Shilinggang demonstrate that people’s diets were mainly dominated by C₃-based foodstuffs, probably due to both direct consumption of C₃ food and as a result of C₃ foddering of consumed animals. Auxiliary C₄ food signals can also be detected. High δ¹⁵N values indicate that meat was an important component of the diet. Analysis of faunal samples indicates that people primarily fed pigs and dogs with human food waste, while sheep/goats and cattle were foddered with other food sources. We compare stable isotope and archaeobotanical data from Shilinggang with data from other Bronze Age sites in Yunnan to explore potential regional variation in subsistence strategies. Our work suggests that people adopted different animal utilization and subsistence strategies in different parts of Yunnan during the Bronze Age period, probably as local adaptations to the highly diversified and isolated environments in the region.</p></div

Isotopic composition and quality indicators of human samples from Shilinggang, Yunnan.

<p>The samples marked in bold italics were found to be contaminated, and were not included in further statistical analysis. Context locations are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177867#pone.0177867.g002" target="_blank">Fig 2a</a>. Among the sample context codes, TNnWn (where n is an Arabic numeral) refers to excavation unit; Mn (where n is an Arabic numeral) refers to the grave number. “Ind.” stands for indeterminate sex.</p

Isotopic composition and quality indicators of animal samples from Shilinggang, Yunnan.

<p>Samples marked in bold italics were found to be contaminated, and were not included in further statistical analysis. Context locations are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177867#pone.0177867.g002" target="_blank">Fig 2a</a>. Among the sample context codes, TNnWn (where n is an Arabic numeral) refers to excavation unit; circles with a number inside refer to the stratigraphic layer.</p

Isotopic value distributions for domesticated animals showing groups with different husbandry strategies.

<p>Isotopic value distributions for domesticated animals showing groups with different husbandry strategies.</p

Plan map of northern Shilinggang and stratigraphic column from unit TN6W3.

<p>a) Plan map of the northern part of Shilinggang. Rectangular excavation units are labeled starting with the letter T. Features include burials (labeled with the letter “M”), pits (labeled with the letter “H”), and building foundations (labeled with the letter “F”). The features shown in Fig 2a are modified from Li et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177867#pone.0177867.ref028" target="_blank">28</a>]. b) Photograph of stratigraphic profile in unit TN6W3 (center right of plan map in Fig 2a). Circles around numbers indicate stratigraphic levels.</p

Comparison of human isotopic signatures for adult vs. child samples.

<p>Comparison of human isotopic signatures for adult vs. child samples.</p

Scatter diagram of human and animal collagen carbon and nitrogen values from Shilinggang.

<p>Analytical error is so small that it is contained within the symbols.</p

The location of Shilinggang and other sites mentioned in the text.

<p>DEM (digital elevation model) data was downloaded from Geospatial Data Cloud (<a href="http://www.gscloud.cn/" target="_blank">http://www.gscloud.cn/</a>), and map features in the figure were modified from Li et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177867#pone.0177867.ref028" target="_blank">28</a>].</p