Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor’s judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models. Lung cancer is one of the most common diseases in the world, accounting for 1.3 million diagnoses and 1.2 million deaths per year  and the prey searching and attacking are implied utilizing the The new local optimal is explored provided that Given that the GWO converges slowly, with a lower convergence rate at the early stage and a higher convergence rate at the latter stage ,35, we uB.Improved grey wolf optimization algorithm-based deep convolution neural network modelThe deep convolution neural network classifier configuration comprises several layers, each of which defines a combination of nodes and functionalities, as depicted in In the above equation, * denotes the convolution layer and lgorithm .The learning behaviour of the lung cancer detection model is improved by integrating a batch normalization layer between the convolution operation and the ReLU layer; as a result, this layer is accountable for regulating both the gradients and the authorizations in the system to ensure efficient training. The local information is transferred from the convolution layer to the quasi-ReLU layer, which has neither mass nor bias. Obtaining bottom-up samples from the returned features from the convolution layer within the max-pooling enables the reduction in information and geographic size. Depending on the preceding layer of the convolutional layer, the identical process of producing the finished piece within the fully connected layer is represented by Equations (22) and (23).This implies that the suggested IGWO algorithm determines the weights of the training DCNN classifier for the optimum solution. Eventually, the output FC layer is regularized using the SoftMax activation function to facilitate efficient classification layer processing . The catThis section describes the experimental setup, the dataset used, the results obtained, and the comparative outcomes in a broader context. The proposed IoT-based lung disease prediction healthcare monitoring system is built on the Android operating system and Python libraries.The proposed IGWO-based DCNN model is trained on the Exasens dataset , a lung The proposed work has been implemented with the incorporation of the microcontroller and other readily available hardware devices, coupled with the LoRa communication hardware that forwards the data to the cloud storage system. Different features such as the patient’s age, chronic diseases, and gender have been saved and combined with the patient’s ID in the suggested system. The data collection and processing are carried out on a Raspberry Pi single-board computer with the hardware configuration described in The proposed IGWO-based DCNN for lung disease is assessed using sensitivity, specificity, accuracy, precision, disease prevalence, and negative predictive value. The outcomes are evaluated against cutting-edge clinical decision support systems (CDSS) , regiona(i)SensitivityThe sensitivity evaluates the various methods by assessing the capacity of the anticipating based on the features acquired against the method’s estimated outcomes, as indicated in Equation (24).(ii)SpecificityThe specificity describes the ratio of true negative values to the total number of false cases in the system, with values of 0.0 and 1.0 for the worst and best case scenarios, respectively, and numerically can be stated as shown in Equation (25).(iii)AccuracyThe accuracy is measured as the proportion of properly classified predictions (points) to the total number of predictions between 0 and 1, and it relies on how the data is normalized for the algorithm, as given in Equation (26).(iv)Precision The precision measures the number of accurate positive predictions made by the algorithm, and it is determined mathematically as the ratio of properly predicted positive instances divided by the total number of positive examples anticipated, as provided in Equation (27).(v)Disease Prevalence The disease prevalence is the clinical prognosis that defines the likelihood of discovering the disease  among patients before the screening test and can be expressed mathematically, as described in Equation (28).(vi)Negative Predict ValueThe negative predictive value (NPV) is the proportion of patients with negative test results who are already healthy, and it is mathematically defined as the ratio of subjects properly classified as negative to all those with negative test findings (Equation (29)).In the above Equations (28) and (29), the In this section, we contrast the proposed IGWO-based DCNN model’s performance to that of the existing CDSS , RCNN 2, ACM 2525, and MIn comparison to existing methodologies, the presented study has greater prediction accuracy, as depicted in The disease prevalence values and prediction values of the suggested and current techniques are compared in In this study, we introduced a novel IoT-enabled healthcare monitoring platform based on an Improve Grey Wolf Optimization (IGWO)-based deep convolutional neural network (DCNN) model. The fundamental mechanism of the Tasmanian Devil Optimization (TDO) algorithm for identifying the most relevant features for diagnosing lung disease is investigated. The standard GWO’s convergence rate is modified, and the resulting improved GWO algorithm is used to fine-tune the parameters of the DCNN model. The suggested framework is developed using the Python DCNN libraries and the Android operating system, and it is tested by employing the Exasens benchmark dataset from the Research Center Borstel and the Bio Material Bank Nord . The dataset consists of demographic data taken from saliva samples of four different groups, including those with asthma, chronic obstructive pulmonary disease (COPD), infection, and normal. Following the simulation outcomes, the suggested IGWO-based DCNN model outperform to the existing CDSS, RCNN, ACM, and Mask R-CNN models in terms of precision, accuracy, sensitivity, and specificity. In more detail, the suggested approaches have precision, accuracy, sensitivity, and specificity of 99.15%, 98.27%, 97.67%, and 98.12%, which are superior to the current CDSS, RCNN, ACM, and Mask R-CNN methods.


Fibromyalgia syndrome (FMS) is a systemic and multifactorial disease of unknown etiology. There are many co-morbidities that the patient presents, making the clinical picture not immediate. Cognitive decline and emotional alteration (anxiety and depression) are found in fibromyalgic patients, as well as temporomandibular joint arthrokinematic disorders, dental malocclusion, and periodontitis. There seems to be a biunivocal relationship between oral and psychiatric dysfunctions in fibromyalgia. The article reviews the information regarding oral health alterations with respect to the patient's cognitive and emotional response, as the most recent information we have raises new hypotheses about the diagnosis of FMS. Fibromyalgia syndrome (FMS) is a systemic disorder, affecting approximately 0.5-6.6% of the world population ,2. The sThe etiology of FMS is multifactorial, with evidence of subgroups of patients with genetic alterations, dysfunctions of hormonal and metabolic systems, and systemic and local neurological disorders . SymptomThere are various scales of evaluation and clinical classification for the patient with FMS; Revised Fibromyalgia Impact Questionnaire (FIQR), Fibromyalgia Assessment Status (FAS), Polysymptomatic Distress Scale (PDS), Nociplastic-based Fibromyalgia Features (NFF), Symptom Severity Score (SSS), Widespread Pain Index (WPI) ,10. FMS Some brain areas that handle pain information are more active than in healthy subjects, such as the posterior insula, secondary somatosensory cortex . Not onlOrofacial sphere dysfunction and fibromyalgiaOne of the dysfunctions that the patient with FMS reports is an alteration of the function of the temporomandibular joint , with a greater prevalence in the female sex . A recenThe relationship between orofacial disorders and fibromyalgia: periodontitisA clinical feature of FMS is the presence of psychiatric disorders , with a three times higher incidence than in the general population . PatientNeuroinflammation causes cognitive impairment and psychiatric disorders . ChronicThe same American College of Rheumatology describes how the presence of temporomandibular joint alterations and dry mouth are clinical indications of the presence of FMS . InfectiThe relationship between orofacial disorders and fibromyalgia: dental malocclusionA dysfunction of the temporomandibular joint (TMJ), and/or the presence of periodontitis can cause alterations of dental occlusion, i.e., a discrepancy in the relationship between the teeth of the upper and lower arches ,29. The A masticatory dysfunction can induce behavioral and cognitive alterations. In an animal model, a malocclusion generates a morpho-functional remodeling of the hippocampus area, reducing memory and learning ability, inducing anxiety and depression; in the human model, an alteration of dental occlusion lays the foundations for dementia . The infThere is a strong connection between proprioception and emotions; not only does a dysfunctional somatic structure generate pain, but also non-physiological emotions. Additionally, when systemic neuroinflammation exists , area S1 is overstimulated . AnotherAnother hypothesis is to understand how a non-physiological proprioceptive stimulation can induce emotional dysfunctions. For example, impaired proprioception is probably an important stimulus for creating or fueling morpho-functional changes in the central and peripheral nervous system, leading to or increasing inflammation and pain, cognitive decline, and behavioral disturbances. In patients with FMS the peripheral proprioceptive system appears dysfunctional Figure  6]..6].We recall that the presence of depression and anxiety in patients with FMS is correlated with cognitive impairment . FurtherThere are many questions still unanswered. To give examples, we do not know the real connections between a malocclusion and the concomitant presence of systemic pathologies  . In an aTo conclude with examples, malocclusion stimulates afferents to the sensory trigeminal nucleus , which projects information to the cerebellum, influencing posture and balance . The diaFibromyalgia syndrome (FMS) is a systemic disorder, with symptoms not only somatic (muscular and joint) but also within the cognitive and psychiatric spheres (anxiety and depression). The correct clinical picture is not immediate and, on some occasions, the diagnosis of FMS can occur after a few years. The article reviewed some information regarding orofacial system dysfunctions, such as periodontitis and dental impaction, related to fibromyalgia. Alterations of the orofacial sphere could be not only the cause of the symptomatological persistence of the fibromyalgic patient but also a cause that would stimulate the onset of FMS. During the patient's visit, the history of the orofacial area should not be missing and should not exclude in advance that the presence of disorders such as periodontitis and malocclusion could be signals to better address the diagnosis. To conclude, dentists should collaborate with rheumatologists if the patient presents multiple symptoms, with the ultimate aim of preventing or better directing the therapeutic procedure. Further effort should be made by the clinician to broaden the knowledge of this pathology.


Arteriovenous malformation (AVM) combined with aneurysms is not uncommon, but AVM of the basilar artery, brainstem, and right middle cerebral artery combined with multiple intracranial aneurysms (IAs) is rare. Cases of aneurysm protrusion into the optic canal are also rare. We report a distinctive case of intracranial AVM combined with multiple IAs and partial protrusion of a cavernous segment aneurysm of the right internal carotid artery into the optic nerve canal.Cases of partial protrusion of a cavernous segment aneurysm of the right internal carotid artery into the optic canal, resulting in widening of the optic canal compared to the contralateral side, compression, thickening and swelling of the subocular veins, and obstruction of venous drainage warrant the clinician’s attention. Intracranial arteriovenous malformation (AVM) combined with multiple intracranial aneurysms (IAs) is a complicated and specific cerebrovascular disease, with an incidence ranging from 2.7% to 16.7% and is a common risk factor for hemorrhage, with a high mortality rate [A 57-year-old man was rushed to the emergency room with intermittent convulsions. The patient had a five-year history of hypertension and a single seizure one year earlier. Axial computed tomography angiography (CTA) image  showed aThe final diagnosis was intracranial AVM combined with multiple IAs and a right internal carotid artery aneurysm. The patient was given antiepileptic, vasospasm-relieving, and blood pressure-controlling medication after being admitted to the hospital. When the patient had stabilized, the patient was transferred to another hospital. However, due to financial constraints, the patient and his family declined the further therapy and were self-discharged from the hospital.The mechanism of AVM combined with IA is complicated. IA mostly occurs in the supplying arteries of AVM and there is a blood flow correlation between IA and AVM. Moreover, fibrous degeneration and thinning of the AVM-supplying arterial walls, disruption of the intima, and alterations in the vascular musculature lead to a significant increase in pressure in the AVM-supplying vessel. These hemodynamic mechanisms may explain the development of AVM combined with IA.Given the patient’s imaging data, this case of AVM combined with IA is difficult to handle because of the following characteristics:IA occurred in the supplying artery of the AVM, which is different from the vascular structure of an ordinary IA and is clinically rare.Besides the IA in the intracranial arteriovenous malformation mass, IA was also present in the distal basilar artery and the right internal carotid artery.The IA of the cavernous segment of the right internal carotid artery partially protruded into the optic canal, resulting in widening of the optic canal compared to the contralateral side and compression, thickening, and swelling of the inferior ophthalmic vein by obstruction of venous outflow.The arteriovenous malformation vascular mass was supplied by the C5–6 segment of the internal carotid artery and the basilar artery, and several drainage veins were seen converging into the superior sagittal sinus. AndIA predominantly occurred in vessels other than ordinary IA vessels .Digital subtraction angiography (DSA), which is the gold standard for the clinical diagnosis of AVM and IA, its use is restricted because it is an invasive examination and more likely to cause cerebrovascular accidents than CTA . However, because CTA is a noninvasive, inexpensive, and quick imaging technique, it can be utilized as a reliable alternative to DSA in emergencies demanding immediate operation . CTA andWe reported a rare case of intracranial AVM combined with multiple IAs and partial protrusion of a cavernous segment aneurysm of the right internal carotid artery into the optic nerve canal, to increase awareness of this rare cerebrovascular disease.


Psychosomatic disorders, their polymorphism and wide distribution in the population are the subject of study by many specialists in borderline mental disorders.We examined 48  children aged 6-13 years who were referred for treatment to a pediatric hospital with suspected cardiac or respiratory pathology.Standard clinical methods  were used. The mental state of children was assessed qualitatively, taking into account the data of psychopathologic, psychological examinations, as well as quantitatively, according to original questionnaires.The clinical picture of the mental state was determined by neurotic disorders of the anxiety-suspecting hysterical type, and in 14% with transient psychotic episodes, qualified as an outpost, the symptoms of an endogenous disease.Neurophysiologic tests revealed disturbances in the process of lateralization, visual perception and information processing with weakness of the right hemispheric, less often left hemisphere functions.Neurological examination revealed some scattered symptoms of minimal cerebral dysfunction, as well as non-localized neurological signs in the area of cerebral innervation, there were signs of mixed vascular dystonia.An analysis of environmental factors showed that children were brought up in conditions of insufficient attention, hypopedia. One third of the cases came from incomplete families. About a quarter of the children grew up in large families, were the youngest children of elderly parents.In heredity, cases of manifest psychosis were not identified. However, an analysis of the personal qualities of parents speaks of schizotypical stigmatization; in almost every family, fathers or mothers had coronary heart disease and joint damage. Insufficient level of education of some parents.In general, the mental state of children, we can conclude that it corresponds to dysontogenetic with a predominance of schizotypical stigmas in half of them, partial underdevelopment of the sensory and emotional-volitional spheres, similar to disorders in children from conditions of maternal deprivation.None Declared


To propel the green transformation of the transportation industry in Guangdong Province. Against the backdrop of the five-year plan, this study employs the Tapio decoupling model and the Logarithmic Mean Divisia Index decomposition method to analyze the decoupling status and driving factors of carbon emissions from transportation and economic growth in Guangdong Province from 2001 to 2020. The results show that:(1) Both the total volume of carbon emissions from transportation and per capita emissions exhibit an overall upward trend, with petroleum-based emissions accounting for an overwhelming 96%, with diesel emissions register the highest proportion and most substantial increase. (2) The decoupling status predominantly manifests as weak decoupling, with intermittent years expansive coupling,, expansive negative decoupling, strong decoupling, and recessive coupling, thus indicating a persistent state of decoupling instability. (3) The study introduces the urbanization effect, wherein the effects of income urbanization, as well as spatial urbanization, predominantly exert positive driving influences. Conversely, the effects of energy intensity, population urbanization, industry scale, and energy structure collectively exert negative driving influences. Notably, the effect of income urbanization emerges as the primary factor propelling the augmentation of carbon emissions, while the energy intensity effect serves as the primary factor curbing such escalation. Finally, pertinent policy recommendations are put forth. The transportation sector in China contributes approximately one-tenth of the country's total carbon emissions and is a fundamental, leading, strategic, and important service industry within the national economy. It is also one of the fastest-growing industries in terms of energy consumption and carbon emissions, making it the second-largest source of global carbon emissions3.Global climate change has emerged as an urgent challenge to human survival and societal progress. During the 75th United Nations General Assembly in September 2020, China announced its commitment to peak carbon dioxide emissions before 2030 and strive to achieve carbon neutrality by 2060. China is currently the world's largest emitter of carbon emissions2 emissions4. With the acceleration of urbanization and the continuous growth of rigid transportation demands, the rapid development of the transportation sector imposes a significant burden on the environment. This will inevitably lead to increased energy consumption and intensified carbon emissions, hindering ecological and environmental benefits and impeding high-quality economic development. Therefore, it is imperative to explore a path of green and sustainable development. Guangdong Province not only leads the country in terms of economic development, but is also a province with a large population, resulting in increased energy consumption and carbon emissions6. Moreover, it was one of the earliest regions in China to carry out low-carbon construction pilot projects, emphasizing environmental protection and implementing a series of measures8. Achieving coordinated development between the transportation sector and energy carbon emissions in Guangdong Province is a key issue in resolving the contradiction between economic growth and environmental protection.From 2000 to 2015, Guangdong Province had the highest carbon emissions from the transportation sector among all provinces in China, accounting for approximately 9.46% of the national transportation sector COTherefore, analyzing the decoupling relationship between carbon emissions from the transportation sector and industry economic growth in Guangdong Province, as well as investigating the driving factors influencing decoupling and their contributions to the decoupling index, will contribute to the promotion of green transformation and sustainable development in the transportation sector of Guangdong Province. This analysis will provide a practical basis for selecting feasible paths and formulating policies for low-carbon development tailored to the transportation sector in Guangdong Province.13. However, with the accelerated urbanization process and the rapid development of the transportation sector, scholars both domestically and internationally have undertaken research on the decoupling status between carbon emissions from the transportation sector and economic development. Compared to the EKC approach, decoupling analysis is considered more straightforward and capable of dynamically revealing the real-time relationship across different years. Consequently, it is widely regarded as a more appropriate method for assessing the relationship between carbon emissions and economic growth15. The concept of decoupling was initially introduced by the Organization for Economic Co-operation and Development (OECD) in their research on energy policy issues. It reflects the degree to which economic development and environmental pressures change in synchrony. The decoupling model enables annual quantitative analysis of the dependency of carbon emissions on economic growth and has significant advantages in terms of computation16. Therefore, in this study, the Tapio decoupling model is employed to analyze the decoupling relationship between carbon emissions from the transportation sector and the economic development of the industry in Guangdong Province.The synergistic development of the transportation sector and environmental governance has long been a focal point of research, and scholars both domestically and internationally have employed various methods to study this topic. Early studies on the relationship between transportation carbon emissions and economic growth primarily focused on issues such as coupling coordination, interactive mechanisms, and the Environmental Kuznets Curve (EKC)17 examined the decoupling relationship between economic growth and carbon emissions in the European transportation sector, proposing a theoretical framework that distinguishes decoupling, negative decoupling, and coupling. It further divides them into eight types, including weak decoupling, strong decoupling, and expansive coupling. Wang et al.18 investigated the decoupling status of China's transportation industry and found that the decoupling status in the eastern region was more stable and ideal compared to the central and western regions. Wang et al.19 applied the Tapio model to study the decoupling relationship between economic growth and carbon emissions in the transportation industry of Jiangsu Province, revealing a distinct cyclical pattern of weak decoupling, expansive negative decoupling, and weak decoupling during the period from 1995 to 2013. Through in-depth analysis, these changes were found to be related to the policies implemented during the same period by the government. Hu et al.20 utilized the Tapio decoupling model to analyze the decoupling status between transportation carbon emissions and economic growth in the Yangtze River Economic Belt from 2004 to 2018. The results indicated that the decoupling of transportation carbon emissions in the Yangtze River Economic Belt was primarily weak decoupling and overall unstable. Yi et al.21 studied data from 30 provinces in China and examined the relationship between the development of the transportation industry and carbon emissions from a provincial perspective using the Tapio model. The findings suggested that underdeveloped provinces were more likely to exhibit a weak decoupling status compared to developed provinces and coastal provinces. It is evident that the overall relationship between transportation sector carbon emissions and economic growth varies among different regions, particularly with significant differences in decoupling status. In this study, we investigate the decoupling relationship between carbon emissions from the transportation sector and economic development in Guangdong Province, taking the five-year plan as the background.In studies on the relationship between transportation carbon emissions and economic growth, Tapio19. The SDA method requires input-output data for decomposing influencing factors and has higher data requirements compared to the IDA method22. On the other hand, the IDA method offers advantages such as simpler data requirements and better data availability. Within the IDA framework, the Logarithmic Mean Divisia Index (LMDI) method27 is widely employed in studies on the changes in energy consumption and environmental pollution in the transportation sector. Wang et al.28 found that energy intensity played a promoting role in decoupling in developed countries in Europe. However, for developing Asian countries, energy intensity acted as a main factor inhibiting carbon emissions. Kim29 discovered that the increase in carbon emissions in the transportation sector in South Korea from 1990 to 2013 was primarily influenced by the effect of economic growth, while the inhibition of carbon emissions was mainly influenced by the energy intensity effect in the transportation sector. Kaya30 argued that population, per capita GDP, energy use per unit of GDP, and carbon emissions per unit of energy use were the four main factors driving carbon emissions in a country or region. Raza et al.31 employed the LMDI model to examine the influencing factors of carbon emissions in the transportation sector of Pakistan, revealing that economic growth was a significant factor promoting carbon emissions. Talbi32 studied the impact of fuel energy consumption, transportation energy intensity, economic growth, urbanization process, and fuel mix on carbon emissions in the transportation sector of Tunisia, with energy efficiency and fuel mix playing dominant roles in reducing carbon emissions. Jia et al.33 applied the LMDI method and took a multi-sectors and multi-stages approach, conducted a study on the driving factors of carbon emissions in China since 1980. The results suggest that the energy and industrial structure within the transportation sector have the capacity to mitigate the increase in carbon emissions. Yang et al.34 utilized the LMDI decomposition model and identified that the sustained expansion of economic scale was the primary driving factor behind carbon emissions growth in the Yangtze River Economic Belt's transportation sector, while the transformation of transportation and economic structure served as key factors inhibiting carbon emissions growth, with technological advancements contributing to the suppression of carbon emissions. Li et al.35 conducted research on the driving factors of carbon emissions in the Beijing-Tianjin-Hebei region's transportation sector, finding that per capita GDP had the largest driving effect on carbon emissions, while energy intensity had the most pronounced inhibitory effect. Yang et al.36 analyzed the influencing factors of carbon emissions in the transportation sector of Jiangsu Province using the LMDI method, revealing that economic output, population scale, and industrial structure positively drove the increase in carbon emissions, while energy structure and energy intensity negatively influenced the increase in carbon emissions. Wu et al.37 applied the LMDI method to conduct a multi-factor decomposition of carbon emissions changes in Shanghai's transportation sector, and the results indicated that both population size and per capita GDP played a promoting role in carbon emissions changes, while energy intensity and energy structure exerted inhibitory effects. As carbon emissions can vary due to different energy structures of the research subjects, this study selects eight energy types and introduces the concept of urbanization to explore the contribution values of driving factors and decoupling indices in transportation carbon emissions in Guangdong Province.In studies on the driving factors of transportation carbon emissions, commonly used research methods include the Structural Decomposition Analysis (SDA) and the Index Decomposition Analysis (IDA)Through a review of previous research, we found that the current research on carbon emissions in the transportation sector has made some progress, but there are also the following three deficiencies and improvements: (1) Existing research on the driving factors of transportation carbon emissions in Guangdong Province has primarily employed the LMDI method for decomposition analysis, lacking the integration with decoupling models. The introduction of decoupling theory provides a new research perspective for analyzing the changes between economic growth and carbon emissions, as well as the calculation and analysis of the decoupling index contribution values of various driving factors. This study adopts the Tapio decoupling model and LMDI index decomposition method, replacing traditional econometric models. Compared to traditional measurement methods, the calculations of the Tapio decoupling model and LMDI method are more concise and can dynamically and instantaneously reflect the annual contributions of various influencing factors to carbon emission changes, providing more intuitive results. (2) Previous research has mainly focused on analyzing the driving factors of carbon emissions in terms of energy structure, energy intensity, industry scale, economic growth, and population change, neglecting the impact of urbanization on the transportation sector. Urbanization in Guangdong Province is rapidly progressing, and considering the rapid expansion of urban space, resident income, population density, and industrial agglomeration, the rapid advancement of the urbanization process has a significant influence on carbon emissions in the transportation sector. Therefore, analyzing the impact of urbanization contributes to a more comprehensive understanding of carbon reduction and decoupling goals in the transportation industry in Guangdong Province. (3) This study takes a comparative analysis approach during the Five-Year Plan period to explore the differences in decoupling status between carbon emissions in the transportation sector and industry economic development in Guangdong Province. Based on this analysis, we delve into the reasons behind these differences during the Fifteen-Year Plan to the Thirteenth Five-Year Plan period, providing references for future studies on transportation carbon emissions in various provinces.This study aims to provide a comprehensive understanding of the decoupling relationship between carbon emissions from the transportation sector and industry economic development in Guangdong Province by utilizing the Tapio decoupling model. Based on the calculation of carbon emissions from the transportation sector in Guangdong Province from 2001 to 2020, the study improves upon the traditional LMDI decomposition model by incorporating the concept of elastic decoupling. The decoupling index is decomposed into six driving factors, with a focus on examining the impacts of urbanization, energy structure, and energy intensity on the decoupling status of the transportation sector in Guangdong Province. By combining the Tapio decoupling model with the LMDI factor decomposition method, a decoupling factor decomposition model is constructed to analyze the decoupling indexes of each factor and quantify their contributions. Finally, policy recommendations and strategies are proposed, providing a scientific basis and assessment reference for the formulation and implementation of policies suitable for the low-carbon development of transportation in Guangdong Province.38. According to the National Greenhouse Gas Inventory Guidelines by the Intergovernmental Panel on Climate Change (IPCC)39, two methods, namely the top-down and bottom-up approaches, are provided for estimating carbon dioxide emissions in the transportation sector. The top-down method involves multiplying the transportation energy consumption within a specific region by the carbon emission factors of various energy sources to obtain the total carbon emissions. On the other hand, the bottom-up method calculates the total energy consumption by multiplying the distance traveled by different modes of transportation with the unit energy consumption per distance, and then multiplying it by the carbon emission factors of various energy sources to obtain the total carbon emissions37. Due to the limited availability of data on vehicle stocks, travel distances, and unit fuel consumption per distance in the bottom-up method, this study adopts the top-down approach to estimate the carbon emissions from energy consumption in the transportation sector of Guangdong province:Due to the inherent mobility of carbon dioxide emissions, the calculation of carbon dioxide emissions in the transportation sector involves more uncertainties compared to other industriesHere, 41 in considering the carbon emissions of the transportation sector in Guangdong Province. The calculation process takes into account eight types of energy sources: raw coal, gasoline, kerosene, diesel oil,, fuel oil, liquefied petroleum gas, natural gas, and electricity. The carbon emission factor for electricity is derived from the average CO2 emission factor of regional power grids in China42. The relevant parameters involved in the calculation of carbon emission factors are presented in Supplementary Table This study takes a more comprehensive approach than previous research43. This article investigates the relationship between carbon emissions in the transportation sector of Guangdong Province and economic development, using the Tapio decoupling model17. The decoupling expression is formulated as follows:The decoupling theory proposed by the OECD aims to depict the disconnection between economic growth and the environment, wherein economic growth no longer entails an increase in resource consumption or environmental pollutionIn the equation, 30 to explore the factors influencing carbon dioxide emissions, expressing carbon dioxide emissions in mathematical relationships with energy, economy, and population. The urbanization rate of Guangdong Province has risen from 55.00% in 2000 to 74.15% in 2020. The rapid development of urbanization has had a significant impact on transportation carbon emissions. Building upon previous research findings44, this study incorporates the concept of urbanization. The changes in transportation carbon emissions caused by economic growth and lifestyle changes during the urbanization process are referred to as income urbanization effects, while the changes in transportation carbon emissions resulting from population density are termed population urbanization effects. Furthermore, the changes in transportation carbon emissions due to spatial expansion are referred to as spatial urbanization effects. Considering the characteristics of carbon emissions in Guangdong Province's transportation sector, the Kaya identity is expanded, decomposing the factors influencing transportation carbon emissions into six elements: energy structure, energy intensity, industry scale, income urbanization effects, population urbanization effects, and spatial urbanization effects. The improved LMDI model is constructed as follows:The Kaya identity is an analytical method proposed by Japanese scholar Yoichi Kaya27, we can perform a decomposition of Eq. (By employing the residue-free LMDI method proposed by Ang B.W.n of Eq.  to expreBy applying logarithmic transformations, addition/subtraction operations, and further decomposition to Eq. , we can In Eq. , \documeIn order to further understand the contributions of various driving factors to the decoupling elasticity index between carbon emissions in the transportation sector and industry economic development, we can substitute Eq.  into Eq.The main focus of this study is to investigate the decoupling relationship and driving factors between carbon emissions in the transportation sector and industry economic development in Guangdong Province. The research covers the period from 2001 to 2020. The energy consumption in the transportation sector includes energy consumption in transportation, warehousing, and postal services. The data on transportation energy consumption are obtained from the China Energy Statistical Yearbook for the years 2002 to 2021. Economic data are sourced from the China Statistical Yearbook, the Guangdong Statistical Yearbook for the years 2002 to 2021. The data on the built-up area of cities are obtained from the China Urban Construction Statistical Yearbook for the years 2001 to 2020. Additionally, to eliminate the impact of price fluctuations on GDP, this study adjusts the actual GDP and the total output of the transportation industry based on the corresponding production indices using the year 2001 as the base year. The selection of carbon emission coefficients is crucial for accurately calculating carbon emissions. To reflect the actual situation in Guangdong Province more accurately, this study adopts carbon emission coefficients from the Provincial Greenhouse Gas Inventory and Energy Statistical Yearbook instead of using data from the IPCC. The average low heating value is sourced from the General Rules for Comprehensive Energy Calculation, while the carbon content and carbon oxidation rate per unit of heat are obtained from the Guidelines for Provincial Greenhouse Gas Inventory Compilation.40 are due to the fact that this paper includes raw coal, liquefied petroleum gas, natural gas, and electricity. Additionally, it analyzes the contribution of each energy source to carbon emissions in the transportation sector. This comprehensive consideration of energy sources aligns more closely with reality, providing a more accurate assessment of carbon emissions. Based on the China Energy Statistical Yearbook and the Guangdong Statistical Yearbook, we have calculated the relevant data for carbon emissions in the transportation sector in Guangdong Province from 2001 to 2020. As shown in Table Specifically, during the period from 2001 to 2010, the total carbon emissions in the transportation sector in Guangdong Province increased by 3181.10 thousand tons, showing a rapid upward trend, and reaching the highest growth rate within the study period in 2005. From 2011 to 2020, the growth rate of carbon emissions did not exceed 9%, with negative growth observed during the periods of 2012–2013 and 2019–2020. The year 2020 marked the lowest growth rate of carbon emissions within the study period. Analyzing the changes in carbon emissions in the transportation sector in Guangdong Province during the study period, several factors may have contributed to these changes. Firstly, during the Tenth Five-Year Plan and Eleventh Five-Year Plan periods, the rapid urbanization and economic growth accelerated, leading to a continuous increase in the rigid demand for transportation, which in turn expanded the demand for energy consumption and contributed to the rise in carbon emissions from Guangdong's transportation sector. Secondly, government authorities have been increasingly focused on energy-saving and emission reduction in the transportation sector, as evidenced by the inclusion of binding targets in development plans during the Twelfth Five-Year Plan and Thirteenth Five-Year Plan periods. Various policies and regulations were introduced to facilitate energy reform and promote industrial restructuring, which, to some extent, mitigated the growth rate of carbon emissions from energy consumption in Guangdong's transportation sector. However, there still exists a significant contradiction between overall economic development and carbon reduction efforts.According to Fig. The decoupling situation between carbon emissions in the transportation sector and industry economic development in Guangdong Province from 2001 to 2020 was calculated using Eq. (From 2001 to 2010, the first phase exhibited three decoupling statuses: expansive coupling, expansive negative decoupling, and weak decoupling, with expansive coupling being the predominant one. The reason for this can be attributed to the emphasis on improving the efficiency of economic growth during the Tenth Five-Year Plan and the Eleventh Five-Year Plan, which led to rapid expansion of industries characterized by heavy industrialization in Guangdong Province. This expansion resulted in an increase in inflexible transportation and logistics demands, accompanied by inadequate transportation infrastructure and a lack of effective emission reduction measures. Consequently, transportation energy consumption was difficult to control, exacerbating carbon emissions from the transportation sector.From 2010 to 2015, the second phase was primarily characterized by weak decoupling, with a strong decoupling status observed in 2012 to 2013, representing an improvement compared to the first phase. The reasons for this improvement can be attributed to the national focus on energy conservation and emission reduction during this period. Guangdong Province began to prioritize low-carbon development and introduced policies and regulations such as the Guangdong Province Development Plan for Energy Conservation and Emission Reduction in the Transportation Sector during the Twelfth Five-Year Plan and the Guangdong Province Implementation Plan for Total Emission Reduction of Vehicle Pollutants during the Twelfth Five-Year Plan. These measures aimed to promote the application of new energy vehicles in the public transport sector, the development and utilization of clean and renewable energy, optimization of freight transportation organization, adjustment of transportation structure, and control of pollution from ocean-going vessels. Additionally, the continuous optimization of industrial structure in Guangdong Province led to a gradual decline in energy consumption per unit of output. These initiatives partially restrained the increase in carbon emissions from transportation, effectively mitigated the growth rate of carbon emissions, and improved the decoupling status between carbon emissions from transportation and industry economic development.From 2015 to 2020, the third phase exhibited three decoupling statuses: expansive coupling, weak decoupling, and recessive coupling, indicating an unstable decoupling status of carbon emissions from transportation. The reasons for this can be attributed to the sustained economic development in Guangdong Province along with intensified environmental governance efforts. Measures such as energy transformation in transportation, acceleration of energy-saving and emission reduction technology innovation, and enhancement of regulatory and service capabilities for energy-saving and emission reduction were implemented as part of the Guangdong Province Development Plan for Energy Conservation and Emission Reduction in the Transportation Sector during the Thirteenth Five-Year Plan. However, during this period, Guangdong Province also faced challenges including a sharp increase in the number of motor vehicles, increased maritime traffic, rapid growth in passenger and freight volume, exponential growth in tourism and logistics transportation, all of which exerted significant pressure on carbon emission reduction in the transportation sector. It is evident that more efforts are needed to achieve substantial reductions in transportation emissions in Guangdong Province. It is worth noting that a recession decoupling status was observed in 2019-2020, mainly due to the economic decline in the transportation sector and reduced passenger and freight volume caused by the impact of the COVID-19 pandemic at the end of 2019.From Table Based on the analysis above, it is evident that the year 2010 marked a turning point, indicating the decisive role of five-year plan policies in carbon emissions reduction. However, the overall economic development of the transportation industry in Guangdong Province still remains dependent on carbon emissions from transportation. Striking a balance between industry economic growth and reducing transportation carbon emissions has been a persistent challenge in the development of the transportation sector in Guangdong Province. In the future, it will be crucial to continue optimizing industrial and energy structures, improving energy efficiency, and adhering to a path of low-carbon transformation and development.40 and Deng et al.41 considered factors such as energy structure, energy intensity, industry scale, economic growth, and population size in their research. In contrast, this paper introduces the concept of urbanization, improves the LMDI model, and obtains the contributions and contribution rates of six driving factors: energy structure, energy intensity, industry scale, income urbanization effect, population urbanization effect, and spatial urbanization effect on carbon emissions in the transportation sector of Guangdong Province, as shown in Table The decoupling elasticity index is limited to studying the synchronous relationship between carbon emissions in the transportation sector and industry economic development, but it cannot explain the mechanism of carbon emission changes. Current research on carbon emissions in Guangdong Province's transportation sector has not considered the impact of urbanization effects. Zhuang et al.According to Table Analyzing the trends of these decomposition effects in relation to the overall effect, it can be observed that the income urbanization effect and spatial urbanization effect are consistently positive, exerting a significant positive driving force on changes in transportation carbon emissions. The cumulative annual effects of these factors are 6375.88 thousand tons and 4716.19 thousand tons, respectively. This indicates that as the residents' living standards improve, there is a substantial increase in transportation demand and energy consumption. The continuous growth in demand for housing mobility and the expansion of urban construction contribute to the increase in transportation carbon emissions. On the other hand, the energy intensity effect, population urbanization effect, industry scale effect, and energy structure effect mostly have negative values. These factors collectively contribute to the negative driving force on changes in transportation carbon emissions, with cumulative annual effects of -3362.16 thousand tons, -2870.24 thousand tons, -597.44 thousand tons, and -153.79 thousand tons, respectively. This indicates that reducing the energy consumption per unit of GDP in the transportation sector, controlling the population density in urban areas, decreasing the proportion of the transportation industry in regional GDP, and optimizing the proportion of different energy sources in total transportation energy consumption will restrain the increase in transportation carbon emissions.40 and Deng et al.41 solely employed the LMDI method to analyze the driving factors behind carbon emissions in Guangdong Province's transportation sector. They lacked integration with the decoupling model. In this study, we have effectively combined the Tapio model with the LMDI method to provide a more intuitive exploration of the contribution rates of various driving factors to changes in carbon emissions in Guangdong Province's transportation sector. In accordance with Eq. (In comparison to previous studies, Zhuang et al.with Eq. , the decThe energy structure effect plays a negative driving role and is one of the factors promoting decoupling. During the study period, the contribution of the energy structure effect to carbon emissions from transportation in Guangdong Province alternated between positive and negative values, resulting in a cumulative negative contribution. This indicates that reducing the proportion of various energy consumption in total transportation energy consumption can restrain the increase in carbon emissions from transportation and promote the decoupling of carbon emissions from transportation and industry economic growth. However, the cumulative contribution rate is only − 3.74%, indicating a very weak effect. Based on Table The negative impact of energy intensity effect plays a crucial role in promoting decoupling. The energy intensity effect decreased from -11.09 thousand tons to -563.78 thousand tons, resulting in a substantial reduction of 552.68 thousand tons. Examining Table Further analysis of Fig. Energy intensity is a key indicator for measuring energy efficiency. From Table The industry scale effect has a negative driving effect and is one of the factors that promote decoupling. During the study period, the industry scale effect on carbon emissions in the transportation sector of Guangdong Province contributed both positively and negatively, with a cumulative negative contribution. This indicates that the industry scale effect has a restraining effect on the growth of carbon emissions in the transportation sector of Guangdong Province. However, the cumulative contribution rate is relatively low at -14.54%, suggesting a limited impact compared to other driving factors. From Table The income urbanization effect plays a positive driving role and is the primary factor hindering decoupling. During the study period, the cumulative contribution of the income urbanization effect was as high as 6375.88 thousand tons, accounting for 155.19% of the total. This indicates that the increase in per capita GDP has led to an increase in carbon emissions from transportation, making the income urbanization effect the dominant factor driving the growth of carbon emissions in Guangdong Province's transportation sector.The contribution of income urbanization effects to the decoupling index aligns closely with the trend of the decoupling index for total transportation carbon emissions. This further underscores that the decoupling status of transportation carbon emissions is primarily influenced by income urbanization effects. During the research period, the per capita GDP of Guangdong Province has experienced steady growth. Correspondingly, transportation carbon emissions increased from 2117.97 thousand tons to 6226.40 thousand tons, an increase of 2.94 times. The improvement in economic development has to some extent raised the living standards of residents, thereby stimulating an increase in consumption demands for enjoyment, such as online shopping and tourism, as well as a rapid growth in private vehicles. With the expansion of transportation demand, it has not only driven the construction of transportation infrastructure and service facilities but also led to continuous growth in transportation energy consumption, further intensifying the pressure to reduce carbon emissions from transportation. Additionally, from Table The population urbanization effect plays a negative driving role and is one of the factors promoting decoupling. During the study period, the population urbanization effect contributed to a total of -2870.24 thousand tons and had a contribution rate of -69.86%. On one hand, the expansion rate of urban areas in Guangdong Province exceeded the population growth rate. The development of urbanization has led to the concentration of urban populations, which helps optimize urban transportation infrastructure layout, accelerate the development of public transportation, and improve transportation efficiency. On the other hand, the population urbanization effect has led to industrial agglomeration, which helps optimize logistics network layout, leverage economies of scale, promote the development of freight and logistics enterprises, and facilitate the low-carbonization of transportation equipment. This has a positive effect on reducing transportation carbon emissions. Furthermore, with the gradual relaxation of China's family planning policy and the accelerated process of population urbanization in Guangdong Province, the scope of urban construction areas continues to expand, leading to continuous population growth and increased transportation volume. Consequently, per capita energy consumption and transportation carbon emissions are bound to increase. Therefore, it is important to pay attention to the ecological and environmental impacts and pressures caused by the population urbanization effect on the increase in carbon emissions from the transportation sector. To promote decoupling between transportation carbon emissions and industry economic growth in Guangdong Province, it is necessary to reasonably control population growth to restrain the growth of transportation carbon emissions.The spatial urbanization effect plays a positive driving role and is one of the factors hindering decoupling. The spatial urbanization effect significantly hampers the decoupling process between transportation carbon emissions and industry economic growth, contributing to a total of 4716.19 thousand tons and accounting for 114.79% of the total contributions, second only to the income urbanization effect. This phenomenon is primarily caused by the continuous expansion of urban space in Guangdong Province as urbanization progresses. This expansion increases the transportation distance between production and consumption locations, leading to a phenomenon of work-residence separation. As a result, the demand for private cars rises, increasing travel distances and adding complexity to the transportation network, thus stimulating a significant increase in transportation demand. Additionally, excessive land development, resource depletion, and lagging infrastructure construction and service facilities in the outskirts of cities contribute to increased energy consumption and carbon emissions in transportation, exerting pressure on carbon reduction efforts and hindering the decoupling of transportation carbon emissions from industry economic growth in Guangdong Province.From 2001 to 2020, both the total transportation carbon emissions and per capita carbon emissions in Guangdong Province showed an overall increasing trend. The growth rate of transportation carbon emissions exhibited a rapid increase from 2001 to 2005, but has been slowing down since 2006. Among various types of transportation energy consumption, carbon emissions from petroleum-based energy consumption contribute the most, accounting for 96% of the total. In particular, diesel oil fuel has shown the largest increase in both quantity and proportion, which is not conducive to accelerating the transition towards green and low-carbon development in the transportation sector of Guangdong ProvinceThe decoupling status between transportation carbon emissions and industry economic development in Guangdong Province from 2001 to 2020 was generally unstable and can be divided into three stages. In the first stage, from 2000 to 2010, the decoupling elasticity index was generally high, showing three states: expansive coupling, expansive negative decoupling, and weak decoupling. The dominant state was expansive coupling, indicating an unsatisfactory decoupling status. In the second stage, from 2010 to 2015, the decoupling index decreased, indicating an improving decoupling status. The main state was weak decoupling, with a strong decoupling state achieved in 2012-2013.In the third stage, from 2015 to 2020, the decoupling index showed a fluctuating upward trend, presenting three states: expansive coupling, weak decoupling, and recessive coupling. The decoupling status remained unstable. During the study period, the decoupling index followed a M curve trajectory, indicating that the overall industry-economic development in the transportation sector of Guangdong Province has not yet overcome its reliance on transportation energy carbon emissions.Overall, urbanization effect has a more significant hindering impact on the decoupling relationship in Guangdong Province compared to non-urbanization effects. Energy intensity effect, population urbanization effect, industry scale effect, and energy structure effect contribute to the decoupling of carbon emissions from economic growth in the transportation sector of Guangdong Province. However, income urbanization effect and spatial urbanization effect act as barriers to the decoupling of carbon emissions from economic growth. During the study period, energy intensity effect played the most crucial role in driving decoupling as a negative factor. Income urbanization effect played a significant role in obstructing decoupling as a positive factor. Energy structure effect made a limited contribution to carbon emissions reduction in the transportation sector. Industry scale effect had a promoting effect on carbon emissions reduction but its impact was less noticeable compared to other driving factors. Population urbanization effect had a minor promoting effect on carbon emissions reduction with a potential for further enhancement in the future. Spatial urbanization effect had a positive driving effect and is expected to remain a hindering factor for carbon emissions reduction in the transportation sector for a considerable period of timeCombining the Tapio decoupling model and the LMDI factor decomposition method, this paper analyzes the decoupling relationship between transportation carbon emissions and economic development in Guangdong Province from 2001 to 2020 as well as the drivers of decoupling, and the main conclusions are as follows:Optimize the industrial development model and strongly support the growth of a low-carbon economy. With low-carbon transformation as the goal, strict adherence to energy-saving and environmental standards is crucial. Continuous efforts should be made to optimize and upgrade the structure of traditional industries and accelerate the reform of high-energy-consuming and highly-polluting sectors, promoting the transition of industries towards energy conservation. Tilted investments, fiscal subsidies, and other measures should be taken to improve the infrastructure for new energy sources, while increasing investment in the funding and talent development of energy-saving and environmental protection technologies. Special attention should be given to the cultivation and attraction of professionals in the field of low-carbon transportation and logistics. Furthermore, the deep integration of next-generation internet technologies with traditional industries should be promoted, facilitating the digital transformation of industries and driving the transition of traditional low-end industries to medium and high-end sectors. Efforts should be made to achieve a coordinated and mutually beneficial progress in carbon emissions reduction and economic and social development. It is important to highlight that the hindering effect of urbanization on the decoupling of economic development and carbon emissions in the transportation sector of Guangdong Province is significantly stronger than non-urbanization effects. Therefore, in the process of advancing urbanization, it is essential to plan urban spatial structures rationally and strengthen overall urban road planningOptimize the energy structure of transportation and establish an efficient transportation system. Control and reduce the consumption of petroleum-based energy by means such as price signaling, lowering the proportion of diesel oil and gasoline in transportation energy consumption. Expand the market for clean energy consumption, such as natural gas, electricity, and solar energy. Accelerate the research and development of new energy vehicles and clean energy vehicles, and promote their use through pilot projects, subsidies, and other measures. Tap into the emission reduction potential of energy structure effect. Improve low-carbon transportation standards and regulatory systems, encourage the development of multimodal transportation, and continue to promote the shift from road to rail and water transport for bulk goods transportation. This will facilitate the low-carbon development of multimodal transportation and comprehensive transportation systems, alleviate the pressure on traditional transportation networks, and improve transportation efficiency to reduce energy consumption and carbon emissionsFocus on technological innovation to enhance energy intensity effects. Under the context of innovation-driven development, actively leverage the opportunities brought by intelligent and digital technologies to promote the transformation of energy towards intelligence, conservation, and low carbon. Accelerate the construction of energy big data platforms, using digital, intelligent, and green smart grids as the foundation. Apply energy big data extensively in production and daily life to achieve rational allocation of energy resources and significantly improve energy efficiency, thereby reducing energy losses and achieving energy conservation and emission reduction goals. Increase investment in energy-saving and emission reduction technology innovation, using scientific and technological innovation and technological progress to promote the improvement of energy utilization efficiency and unlock the emission reduction potential of energy intensity effects. Strengthen international cooperation on carbon emission reduction technologies, making technological innovation a strong engine for carbon reduction. Introduce advanced clean energy technologies from abroad to achieve diversified energy supply and reduce reliance on traditional high-carbon energy sources. Technological progress can continuously enhance the energy-saving potential in energy consumption, effectively reducing end-use energy demandPromote the concept of green transportation and advocate a low-carbon lifestyle. Expand green transportation awareness campaigns to guide the public in choosing low-carbon modes of transportation such as public transit, new energy vehicles, and shared bicycles, while reducing unnecessary transportation demand. Utilize advanced communication and networking technologies to reduce travel distances and frequencies, making transportation more environmentally friendly and travel more low-carbon. Encourage public participation in environmental governance, actively supervise and report high-emission and high-pollution issues, expand reporting channels, and increase penalties, achieving a low-carbon and environmentally friendly society through collective efforts. Make full use of market mechanisms such as carbon emissions trading and carbon taxes, increase vehicle purchase taxes and fuel surcharges, and promote the shift of transportation demand from energy-intensive and polluting road and air transport to low-carbon and environmentally friendly railway, waterway, and urban public transportation modes.Based on the above findings of this paper, the following policy recommendations are proposed to promote the decoupling of economic development and carbon emissions in the transportation industry in Guangdong Province:Supplementary Information.


BackgroundThe cost of critical illness treatment is generally recognized as expensive and increasing in India. Critical illness of the individual will affect the socioeconomic status of the individual and the family. The direct and indirect costs of intensive care and its impact on the socioeconomic status of critically ill patients and their families need to be estimated. The present study was carried out to evaluate the socioeconomic burden of critically ill patients admitted to ICUs in Eastern India.MethodsA descriptive survey was conducted to measure the socioeconomic burden. One hundred fifteen critically ill patients and their family members were conveniently selected for the study. Critically ill patients admitted to ICUs and those who were bedridden for more than seven days along with anyone the family member, i.e., spouse, father, or mother, were included in the study to estimate the impact of long-term illness on the care providers in the family. Socio-demographic and socioeconomic burdens were analyzed through the interview method.ResultsHalf (49.6%) of the critically ill patients were heads of the family, and their employment is the primary source of income for the family members. Most (60.9%) of the patients belonged to lower socioeconomic status. Critically ill patients spend a maximum (38169.6±3996.2) amount for pharmaceutical expenses. Eventually, the family members accompanying patients lost maximum working days because of the long length of hospital stay. Below upper-lower (p=0.046) class socioeconomic family, age less than 40 (p=0.018) years, and those families depending (p=0.003) on patients' income significantly reported higher socioeconomic burden.ConclusionsCritical care hospitalization of patients increases the socioeconomic burden on the whole family, especially in lower-middle-income countries like India. It soberly affects younger age group patients with low socioeconomic status and families depending on the patient's income during their man days. India has one of the most highly privatized healthcare systems in the world . PrivateThe provision of cost-effective intensive care and reduction of OOP expenditure of families of patients admitted to ICUs pose a challenge in India. The major reason could be the lack of penetration of government-funded insurance schemes. Despite several government-funded health insurance schemes (GFHI) available in India, OOP expenditure is still high. This may lead to inequitable access to health care services by the poor. The penetration of GFHI was also skewed. Non-poor and urban take the lion's share as compared to their counterparts. It implies the failure of schemes to reach the poor, who spend a major share of their household income during hospitalization. The household survey 2017-2018) revealed a wide variation between claims of official government data and household surveys in terms of GFHI coverage in India .Notably-2018 revNeed for the studyThe lack of insurance, direct cost of ICU care, and indirect costs such as stay, travel, job loss, rehabilitation, and follow-up care may generate a significant socioeconomic burden on the family of a patient receiving ICU care. Although there was a lot of literature on the burden of caregivers and families of ICU survivors, the study examining the socioeconomic burden of patients' families admitted to public hospital ICUs was limited. In addition, the nature and characteristics of the burden of patients' families admitted to public hospitals ICUs are unique due to the cost of the procedure, diet, essential medication, investigations, sophisticated equipment, and life-sustaining treatments being borne by the government. However, long queues, uneven distribution of tertiary care facilities, accessibility issues, care continuity, delayed treatments, and indirect costs such as travel, stay, caregiver expenses, and loss of daily wages may contribute to a significant burden to the families.Hence, this study aimed to assess the socioeconomic burden of critically ill patients admitted to the tertiary care hospital in Bhubaneswar.Study designA descriptive survey was conducted, and convenience sampling was used to recruit the family members of critically ill patients.Inclusion criteriaCritically ill patients bedridden for more than seven days in the ICU, aged more than 18 years, along with any family members, i.e., father, mother, or spouse, were included in the study. A total of 115 family members of critically ill patients were enrolled in this study.Exclusion criteriaPatients aged under 18 and over 90 years and pregnant females were excluded from the study.Study settingThe study was carried out in tertiary care teaching hospital in Odisha, an Indian state located in the Eastern part of the country. It is a 1,018 noncritical care and 109 critical care bedded hospital. Also, it has all the specialty and super specialty critical beds like medical ICU, surgical ICU, neuro medicine and neurosurgery ICU, cardiology and cardiothoracic ICU, pediatric ICU, respiratory ICU, gastro surgery ICU, transplant ICU, hemato-oncology ICU, trauma ICU, and steps down ICU. Ethical clearance was obtained from the Institutional Ethical Committee  and written informed consent was obtained from the study subjects.Study tools and method of data collectionInvestigator developed the tool and systematically tested its validity and reliability. The tool consists of four sections, section A includes the socio-demographic characteristics of the study participants, section B includes the economic status of the family, section C includes direct and indirect cost expenditure of the treatment, and section D includes the socioeconomic burden on the patient and family. After screening the study participants based on inclusion and exclusion criteria, informed consent was taken, and a 25-30-minute face-to-face interview was conducted based on the tool.Socio-demographic dataTable Table Socioeconomic burden on the patient and family membersTable Association between the socioeconomic burden of critically ill patients with their demographic variablesTable The social and economic burden can be associated with any illness. Critical illness can affect any individual, including the most economically productive individuals in the family. Treatment of critical illness is associated with huge healthcare expenses to the patients and families that include both direct and indirect costs. Direct costs in the form of hospital admission, treatment, medications, transportation, cost of food and shelter to family members, and indirect costs such as missed school days or work and loss of work during the illness, hospitalization, loss of potential income for patient and caregiver and are often associated with morbidity and mortality.Economic impact of critical illness on patients and familiesThis study was carried out in a government tertiary care hospital among critically ill patients and their family members. The cost of the treatment of the admitted patients was borne by the government for BPL patients and through government-funded health insurance schemes (GFHI) for eligible patients. The cost of treatment for other patients was borne by themselves on nominal charges. Other expenses for additional medications, transport, food, and shelter for family members were not covered under these schemes and were borne by patients and their families from their savings. The mean institution cost was INR 6404 US$81.81), including the cost of investigations and treatment procedures not covered under these BPL and GFHI schemes. The results of a retrospective study done in the US show that 13.4% of hospital costs are accountable for intensive care 1.81, inc. The ICUSocial impact of critical illness on patients and familiesThe mean age of the patients was 42 years. A total of 77% of patients belonged to the economically productive age group between 19 and 59 years. They are also the breadwinner on whom the survival and development of children and families depend. In about 49% of families, the patient himself was the head of the family. Around 48% of families had more than five dependent family members, which also increases the economic burden on the family. Loss of wages and productivity losses were measured as the lost man-days from productive activities because of illness and hospitalization. Also, those families solely depending on the patient's income and those aged less than 40 significantly reported a higher socioeconomic burden.The mean man-days lost was 17 days because of hospital visits before admission. The average man-days lost by the patient was 35 days during hospitalization, and the average man-days lost by the family members on account of care of the patients was 67 days. When the patient becomes ill and is hospitalized, the patient himself loses man-days of employment, and the family members, on account of attending to the patient, also suffer from a loss of man-days of employment which will eventually decrease the family's income and lead to the financial burden on the family.From these observations, it is clear that even though diagnosis and treatment are offered free of charge to patients attending government healthcare institutions, a significant financial loss is experienced by the patient's families. Also, the direct and indirect costs may vary based on the nature of the critical illness and the length of stay in the hospital or ICU. When the hospital stay increases, the risk of financial burden also increases for patients and their families. The potential risk of health-related work impairment due to post-critical illness also contributes to lowering the income and increases the economic burden on patient families.There is a greatest need and challenge to decrease the money spent out of their pockets during hospitalization with critical illness in ICUs. The government-funded insurance schemes must meet the patients' unmet financial needs, eventually decreasing the potential risk of poverty in the rural population during critical illness.LimitationsIn this study, we did not assess the direct costs spent by patients' families for diagnosis and treatment before hospitalization or ICU admission in government healthcare centers. Also, we did not assess the costs and financial loss that occurred after discharge.To generalize these findings, a similar study with a large sample size, including public and private healthcare centers, might assess the financial gap between healthcare's direct and indirect costs borne by the patients covered under government health schemes and other insurances.The study identifies that direct and indirect costs constitute the highest share of the total costs associated with critical illness for patients and their families. The socioeconomic effect of intensive care admissions extends beyond individuals or family members, especially in developing countries like India. Family members of critically ill patients admitted to the ICU experience significant problems in regard to psychological and social functioning. Further investigation is required to adopt effective policies to help the patients financially and positively impact family members during critical illness.


The present article discusses the role of light in altering autophagy, both within the outer retina  and the inner choroid . Here autophagy is needed to maintain the high metabolic requirements and to provide the specific physiological activity sub-serving the process of vision. Activation or inhibition of autophagy within RPE strongly depends on light exposure and it is concomitant with activation or inhibition of the outer segment of the photoreceptors. This also recruits CC, which provides blood flow and metabolic substrates. Thus, the inner choroid and outer retina are mutually dependent and their activity is orchestrated by light exposure in order to cope with metabolic demand. This is tuned by the autophagy status, which works as a sort of pivot in the cross-talk within the inner choroid/outer retina neurovascular unit. In degenerative conditions, and mostly during age-related macular degeneration (AMD), autophagy dysfunction occurs in this area to induce cell loss and extracellular aggregates. Therefore, a detailed analysis of the autophagy status encompassing CC, RPE and interposed BM is key to understanding the fine anatomy and altered biochemistry which underlie the onset and progression of AMD. The autophagy machinery is fundamental in promoting visual processing and preserves the integrity of the outer retinal segments. This is mainly due to light-dependent biochemical cascades placed downstream of autophagy activation, which consist of various pathways mostly placed within the retinal pigment epithelium (RPE). Nonetheless, other structures placed at the choroid–retinal border are modulated by light, when regarding the autophagy machinery. In fact, autophagy is abundant at this level, where actual photo-transduction takes place, and is in need of trophic support and high protein/organelle turn-over to sustain light stimulation. This contributes to defining the inner choroid/outer retina as a sort of morpho-functional unit, which may be termed the retinal neurovascular unit  represents the most common cause of blindness in the Western World ,88,89,90This may explain why polymorphous aggregates may develop on both sides of BM in advanced stages of AMD. In fact, an updated perspective considers drusen, beyond the protein and specifically beta-amyloid content, as a complex aggregate of lipid droplets, sugars, melanin, lipofuscin, chromatin vesicles , degenerated melanosomes, degenerated mitochondria, aqueous vesicles, and ultrafine cellular fragments. These drusen are expected to disrupt the BM causing collagen degeneration and loss of elastin, which in turn produces a loss of anatomical integrity. This allows drusen content and cellular debris from necrotic RPE cells to move across BM from the retina towards the choroid . The sceIn fact, Chang et al.  found thDespite already evidenced in the previous part, where the role of the massive engagement of autophagy was evidenced, even in baseline conditions within RPE cells., the classic hallmark of AMD consists of altered RPE cells, where an early and severe defect in the autophagy machinery is well-documented. In fact, in baseline conditions, RPE cells undergo an excess of metabolic activity due to their multiple functions. Namely, most of the autophagy machinery in the RPE is dedicated to the removal of the outer segment of photoreceptors .The engulfment of the autophagy process in the RPE occurs when autophagy is inhibited. This occurs when lysosomes cannot degrade metabolic by-products. This condition is typical of AMD at the early stages, where stagnant lysosomes are present in excess, containing non-digested material in the form of lipofuscins or lipo-melano-fuscin . In factThe occurrence of autophagy dysfunction within RPE from AMD patients emerges in several studies carried out in the last few years ,56,95. TAn autophagy defect within RPE may lead to a number of downstream deleterious events. In fact, altered organelles and misfolded proteins, sugars and lipids are expected to trigger direct toxicity in the cell. This is likely to occur at short time intervals. The late fate of these aggregates is more questionable, since a number of reports range from a detrimental mechanical effect to an inert role as innocent bystanders, simply witnessing a previous dynamic of the disease.Thus, it is likely that a loss of the primary metabolic function, which is bound to an autophagy defect, is expressed in the disease generation more as a dysfunctional molecular defect  rather than a morphological aberrancy (neuropathology). In this way, misfolded proteins or dysfunctional mitochondria are likely to exert their toxicity before being encapsulated within lysosomes or drusen, at early stages, when they are still playing a role in the visual processing, being free in the cytosol before their aggregation. This hypothesis contradicts the common belief that an autophagy defect is considered not dangerous at the early stages, but only later when the volume of aggregates of autophagy-dependent structures is noticeable. According to the classic perspective, within the retina, this volume effect is commonly expected to alter the planar arrangement of the photoreceptors. This was commonly considered to produce visual distortion, such as metamorphopsia, which is quite typical in AMD ,93. In t2) and surpasses by 17-fold the drusenoid area measured in patient 2 (1.32 mm2). Remarkably, such a serious drusenoid involvement in patient 1 occurs along with better visual acuity and less severe metamorphopsia compared with patient 2, affected by a mild drusenoid involvement. The drusenoid area represents the best index to express quantitatively the amount of retina filled with drusen. This index is calculated by multiplying the drusen number and each drusen size. This measurement is more accurate than the simple drusen number or drusen size per se, or a subjective drusen scoring system as extensively discussed previously [As extensively reported in a previous manuscript  and in Teviously . The cenMolecular events altering vision may also occur in cell types beyond RPE. In fact, the occurrence of autophagy involves altogether the inner choroid and outer retina. This is supposed to affect an anatomical unit, which is coupled to match photoreception with metabolic requests and blood supply, as originally postulated by Spraul et al. .This concept of neurovascular unit should be considered as an orchestrated activity of the blood vessels in the inner choroid to supply the metabolic demand of the RPE cells and outer photoreceptors in a way which is coordinated. In this way, when a high energy demand in the outer retina occurs, a cross-talk between RPE and inner choroid generates an appropriate modulation of blood flow and clearance of retina-dependent catabolites. Similarly, the status of the inner choroid is expected to modulate the activation of the outer retina through a specific signaling. In this way, the inner choroid/outer retina unit should be considered as a functional unit, meaning an area where the RPE and the inner choroid work in synergy. This hypothesis makes the inner choroid/outer retina unit similar to the neurovascular unit recently described in the CNS . In lineThis in turn depends on the activity of pericytes and endothelial cells. The kernel of such a coordinated activity may largely depend on a synchronized autophagy status between all these cell types.Light-induced autophagy activity may be the key in triggering such a coordination. This may explain why autophagy machinery in the retina is finely tuned at the level of gene and protein expression to allow visual acuity ,115. In The hypothesis of a cyclic regulation of the “inner choroid/outer retina” neurovascular unit by the autophagy status is further confirmed by the studies of Datta and colleagues , who attThus, it is not surprising that daily light, as much as artificial light, depending on their specific wave-lengths, may have robust epigenetic effects in the outer retina. This is needed to cope with light-induced metabolic demand, the process of vision, and oxidative damage. In fact, light regulates the expression of 23 autophagy-related genes . In detrThe specific role of autophagy in the visual process also relies on the downstream between autophagosomes with lysosomes. In fact, Santo and Conte  indicateThe energy loss which accompanies autophagy activation and visual processing, combined with the need for light in order to activate autophagy in the ON retinal field, may explain why light exposure in order to be beneficial needs to be administered according to a pulsatile pattern, where blood flow fueling energy is coordinated. Similarly, due to the specific sensitivity of various photoreceptors to various wave-lengths, it is expected that, in order to sustain retinal integrity and visual functions, various wave-lengths should be combined during pulsatile light exposure, as recently suggested . The marWhen discussing the effects of light in stimulating autophagy in the retina, it is important to emphasize which specific wave-lengths may sort these effects. In fact. the strong epigenetic stimulation of autophagy is induced in the range between amber, red and infra-red light ,120,121 Although most studies focus on the transition zone between the epithelial/mesenchymal part of the eye where the choroid-retinal junction occurs ,93,95, fIn keeping with retinal neuroanatomy, a key role may be attributed to those glial cells which connect the outer retina with its inner part. In fact, the autophagy status within Muller cells is likely to be relevant to trans-synaptically modulating retinal homeostasis. It is shown that Muller cells, depending on their autophagy status, may induce neo-angiogenesis and inflammation, which occurs during retinal degeneration . Even MuThe spreading of autophagy-dependent retinal activity to the inner retina may also occur as the consequence of the natural stream of excitation and inhibition through bipolar, amacrine, and down to ganglionic cells. In fact, the concept of retinal fields refers mostly to the downstream of glutamate receptors placed on bipolar cells, suggesting a patchy pattern of retinal degeneration, depending on this fact. It is not surprising, indeed, that retinal degeneration in the course of AMD is patchy by definition. The ability to establish a zonal distribution of light-induced autophagy modulation is expected to foster innovative research studies aimed at addressing site-specific autophagy failure in the course of retinal degeneration, in order to better comprehend the molecular mechanisms underlying site-specific retinal degenerative phenomena in AMD. This improved knowledge is expected to improve the treatment of retinal degeneration, mostly AMD, through a site-specific (macular) and wavelength-selective (red) light stimulation aimed at modifying the progression of the disease.