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Several automakers are recalling certain models because they may pose a potential safety risk to owners, according to the National Highway Traffic Safety Administration (NHTSA).

Brands like BMW, Chrysler and Ford are recalling some vehicles due to safety hazards.

Here’s what you need to know to keep your family safe:

BMW

Around 36,922 of BMW’s 2025-2026 X3 vehicles are being pulled because unexpected steering wheel movement may occur while the vehicle is stopped.

No injuries have been reported as of this writing.

The NHTSA said the steering wheel software will be updated by a dealer or over the air, free of charge.

Notification letters are expected to be mailed on Feb. 2, 2026. That is also when Vehicle Identification Numbers (VINs) involved in the recall will be searchable on the NHTSA database.

Owners may contact BMW customer service at 1-800-525-7417.

You can find the recall information here.

Honda

Around 70,658 of the company’s 2016-2020 Acura ILX vehicles have an internal brake leak, which may reduce brake function and increase the risk of a crash.

No injuries have been reported as of this writing.

Dealers will replace the affected parts at no cost. Interim notification letters letting owners know of the safety risk are expected to be sent on Feb. 2, and a second letter will be sent once the final remedy is available.

VINs involved in the recall are now searchable on the NHTSA database. Honda’s number for this recall is RN5.

Owners can call the company’s customer service line at 1-888-234-2138.

You can find the recall information here.

Ford

The company is recalling around 32,160 of its 2022 to 2025 E-Transit vehicles because it may lose drive power and could roll away while parked if the parking brake is not applied.

The NHTSA said no crashes have been reported as of this writing.

The remedy for this recall is still under development. Notification letters letting owners know of the safety risks are expected to be mailed on Monday, Dec. 22, and another letter will be sent when the final remedy is available.

Ford’s number for this recall is 25SD9. VINs involved in this recall are now searchable on the NHTSA database.

Owners with questions can contact Ford’s customer service line at 1-866-436-7332.

You can find the recall information here.

Around 272,645 of Ford’s vehicles are being recalled because the park function may not work properly, potentially causing them to roll away.

No injuries or crashes have been reported as of this writing.

The following models are affected, according to the NHTSA:

2022-2026

2024-2026

2025-2026

The park module software will be updated by a dealer or through an over-the-air update, the NHTSA said. Notification letters are expected to be sent on Feb. 2, 2026, with an additional letter being sent once the final remedy is available.

Ford’s number for this recall is 25C69. VINs involved in this recall will be searched on the NHTSA database starting on Jan. 26.

Owners with questions can contact the company’s customer service line at 1-866-436-7332, the NHTSA said.

You can find the recall information here.

Chrysler

Around 52,565 of the company’s vehicles are being recalled because a software error may prevent the driver’s side air bag and/or seat belt pretensioner from deploying during a crash.

No injuries have been reported as of this writing.

The NHTSA listed the following models as affected:

2025

• Ram 2500 Pickup

• 3500 Pickup

• 3500 Cab Chassis

• 4500 Cab Chassis

• 5500 Cab Chassis

Dealers will update the software for free. Notification letters are expected to be sent on Jan. 15, 2025, the NHTSA said.

VINs are now searchable on the NHTSA database. The company’s number for this recall is C3C.

Owners with questions can contact Chrysler’s customer service at 800-853-1403.

You can find the recall information here.

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Read more on KSAT:

Core characteristics of the FINDERI cohort

Demographics

A total of 504 patients were included in our cohort analysis (Table 1); 491 of them could be thoroughly evaluated for the presence of POD (Supplementary Table 2). Thirteen patients were excluded from the detailed statistical analysis as a thorough POD assessment was not possible (for dropout analysis see supplementary Table 3). Among them, 106 patients (21.6%) were diagnosed with POD following cardiac surgery, based on the CAM-ICU and I-CAM. Detailed demographic data is presented in Table 1. Patients diagnosed with POD were significantly older than those without POD (mean age with POD: 71.0 ± 7.7 years vs. mean age without POD: 67.6 ± 8.3 years, p < 0.001, Table 1). Notable statistical differences were also found between the two groups in terms of MoCA findings, heart failure, implanted pacemaker or defibrillator, heart valve disease, the presence and severity of mitral valve disease, and type 2 diabetes, as detailed in Table 1. POD lasted an average of 3.3 ± 1.3 days, diagnosed 1.4 ± 0.7 days after cardiac surgery and ended 3.8 ± 1.3 days after intervention. Most patients exhibited hypoactive delirium symptoms (45%). A form of mixed POD occurred slightly less frequently (37%); the fewest patients presented with hyperactive POD (18%). POD was diagnosed in 73% of patients via a positive CAM-ICU score and 47% applying the I-CAM), where some patients were positive in both CAM-ICU and I-CAM.

Preoperative heart and brain disease related factors

We conducted univariate logistic regression analysis to identify significant heart and brain disease related factors associated with the development of POD in those patients assessable for POD (n = 491, see supplementary Table 2). The analysis revealed several relevant factors: age (OR 1.05, 95%-CI: 1.02, 1.08, p < 0.001), MoCA assessment results (OR 0.86, 95%-CI: 0.81, 0.92, p < 0.001), the presence of heart failure (OR 2.29, 95%-CI: 1.26, 4.47, p = 0.01), heart valve disease (OR 1.73, 95%-CI: 1.09, 2.81, p = 0.024), mitral valve insufficiency (OR 1.75, 95%-CI: 1.12, 2.72, p = 0.013), mitral valve stenosis (OR 7.22, 95%-CI: 1.17, 55.9, p = 0.033), moderately severe mitral valve disease (OR 3.8, 95%-CI: 1.78, 8.00, p < 0.001), extremely severe mitral valve disease (OR 2.03, 95%-CI: 1.08, 3.74, p = 0.025), tricuspid valve insufficiency (OR 1.7, 95%-CI: 1.02, 2.82, p = 0.038), moderate tricuspid valve disease (OR 3.13, 95%-CI: 1.09, 8.66, p = 0.028), and type 2 diabetes (OR 2.12, 95%-CI: 1.35, 3.31, p < 0.001). Additionally, the presence of a defibrillator was identified as a significant factor (OR 4.76, 95%-CI: 1.24, 19.5, p = 0.022). These findings are presented in supplementary Table 2.

Plasma biomarker

Plasma biomarkers p-tau181 and IL-6 were evaluated via univariate regression analysis to determine their significance in predicting POD in our cohort of 491 patients with assessed POD status (Supplementary Table 2). Our analysis indicated that preoperative IL-6 levels represented a significant risk factor (OR 1.38, 95%-CI 1.07, 1.77; p = 0.012, n = 476), while postoperative IL-6 levels (OR 1.07, 95%-CI 0.77, 1.47; p = 0.69, n = 425), and the difference between postoperative and preoperative IL-6 levels (OR 0.88, 95%-CI 0.68, 1.12, p = 0.29, n = 422) did not constitute significant risk factors in a univariate context (Supplementary Table 2). Similarly, preoperative levels of p-tau181 (OR 2.26, 95%-CI 1.46, 3.57; p < 0.001, n = 483) were found to be a significant predictor of POD in the FINDERI cohort.

Plasma biomarker to predict postoperative delirium

The levels of preoperative IL-6 and preoperative p-tau181 were identified as significant predictors of POD. This is illustrated in Fig. 1; Supplementary Table 4. Supplementary Figure 1 also show the log-transformed biomarker levels of IL-6 and p-tau181. ROC analysis yielded an AUC of 0.605 (95%-CI: 0.544, 0.663, p = 0.0018) for preoperative IL-6 levels and an AUC of 0.641 (95%-CI: 0.581, 0.698, p < 0.0001) for preoperative p-tau181. The optimal cut-off value for preoperative IL-6 was 4.71, demonstrating a sensitivity of 60.4% and a specificity of 58.9%, as detailed in supplementary Table 5A (for postoperative IL-6 cut off value, see Table 4B). The optimal cut-off value determined for preoperative p-tau 181 was 1.57, with a sensitivity of 61.5% and a specificity 60.7%, as reported in supplementary Table 5C. However, it was observed that POD could not be predicted based on postoperative IL-6 levels or the difference between postoperative and preoperative IL-6 levels, as indicated in supplementary Table 4, supplementary Figures 1B, C and Fig. 1. Furthermore, a linear mixed-effects model for IL-6 demonstrated that while time (p < 0.0001) and POD (p = 0.03) were significant factors, the interaction between time and POD was not significant (p = 0.09), as depicted in supplementary Figure 2. The mean group values in the difference of the log-transformed IL-6 levels for no POD were 1.809 (95%-CI: 1.71, 1.91, p < 0.0001) and 1.624 for POD (95%-CI: 1.43, 1.82, p < 0.0001).

Predicting postoperative delirium by combining plasma biomarkers and heart and brain as well as immunotherapy related factors

To identify possible associations among biomarkers and various heart and brain as well as immune system related factors, we conducted a multiple logistic regression analysis in three different models (Table 2A). In the first model, IL-6, age, sex, tumor, corticosteroids, colchicine and cytostatic drugs were included as explanatory variables in the analysis; we found that age and log-transformed preoperative IL-6 facilitated the prediction of POD (Table 2B and Fig. 2, Model 1). We investigated the immunotherapy-associated variables in a model with IL-6, since the activity of the inflammatory marker IL-6 can be influenced by immunotherapy. Our first model was able to predict POD with reasonable accuracy, achieving an AUC of 0.658 (95%-CI: 0.596, 0.714, p < 0.0001), as shown in Table 2B and Fig. 2, Model 1. Log p-tau181, age, gender, cognitive performance measured by MoCA and education were included as explanatory variables in our second model. It also included cognitive performance together with preoperative p-tau181 levels, as neurodegenerative processes are often associated with reduced cognition. This model revealed that log-transformed preoperative p-tau181, female sex, and cognitive performance were relevant factors. POD could be predicted with this model with moderate accuracy, namely an AUC of 0.694 (95%-CI: 0.637, 0.747, p < 0.0001, Table 2C, Fig. 2, Model 2). When we combined all explanatory variables (log-transformed IL-6, log-transformed p-tau181, age, sex, tumor, corticosteroids, colchicine, cytostatic drugs, MoCA findings, education, preoperative log IL-6 and preoperative p-tau 181 and female sex) into one model (Model 3), female sex, education > 10 years and cognitive performance (MoCA) proved to be relevant factors. However, the interaction between the blood biomarkers p-tau181 and IL-6 in blood plasma was not a relevant POD predictor (OR 0.87, 95%-CI: 0.48, 1.56, p = 0.63, Table 2, Model 3). With this combined model 3, POD was predictable with moderate accuracy with an AUC of 0.709 (95% CI: 0.651, 0.763, p < 0.0001; Table 2D, Fig. 2, Model 3).

Predicting postoperative delirium taking a machine learning approach

Decision tree

To identify the most important guidelines for predicting POD, we created a decision showing four important rules. The rules were applied in the following sequence: (1) preoperative p-tau181 level greater than 1.4, (2) the presence of moderate or severe mitral valve disease, (3) a preoperative p-tau181 exceeding 1.8, and (4) preoperative IL-6 level above 5.8, as depicted in Fig. 3. The performance of this decision tree model was quantitatively evaluated, showing an AUC of 0.672 (95%-CI: 0.604, 0.735, p < 0.0001) on the training set and 0.642 (95%-CI: 0.537, 0.738, p = 0.0108) on the validation set. These results, along with detailed visual representations, are provided in supplementary Table 6, Fig. 3.

LASSO

During the application of the LASSO machine learning procedure, non-zero regression coefficient estimates were observed for two variables: age and preoperative p-tau181 levels. The performance of the LASSO model in predicting POD was assessed using ROC analysis, which demonstrated an AUC of 0.751 (95%-CI: 0.686, 0.805, p < 0.0001) for the training and an AUC of 0.652 (95%-CI: 0.539, 0.747, p = 0.0086) for the validation set, reflecting a moderate level of prediction accuracy. These findings, along with the corresponding graphical representation, are detailed in supplementary Table 6 and Fig. 4A, B.

Dec. 22, 2025 – (SAN ANTONIO) – – CPS Energy offices, call center, and customer service centers will be closed for the Christmas holiday on Wednesday, December 24, and Thursday, December 25, as well as for the New Year holiday on Wednesday, December 31, and Thursday, January 1, 2026.  

CPS Energy’s customer service centers will resume normal operations at 7:45 a.m. on Friday, January 2, 2026. 

During the closure, customers can report emergency natural gas or electric notices by calling (210) 353-HELP (4357). Online services may be accessed at cpsenergy.com. 

Customers can conveniently take care of the following services at any time by logging into Manage My Account: 

• start/stop/transfer service, 

• make payment arrangements, 

• reconnect services following full payment of past-due bill 

• sign up for emergency alerts, and 

• update contact information, mailing address, and other options. 

CPS Energy has expanded its Energy Angels program to provide more donation options.   Those interested in giving the gift of energy this holiday season can visit the Energy Angels webpage.    

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About CPS Energy 
Established in 1860, CPS Energy is the nation’s largest public power, natural gas, and electric company, providing safe, reliable, and competitively-priced service to more than 970,000 electric and 390,000 natural gas customers in San Antonio and portions of seven adjoining counties. Our customers’ combined energy bills rank among the lowest of the nation’s 20 largest cities – while generating $10.1 billion in revenue for the City of San Antonio since 1942. As a trusted and strong community partner, we continuously focus on job creation, economic development, and educational investment. We are powered by our skilled workforce, whose commitment to the community is demonstrated through our employees’ volunteerism in giving back to our city and programs aimed at bringing value to our customers.